R Notebook
Milestone 2
Section 1: Update
The main updates I have for Milestone 2 are: I have ran through the entire vignette and have added analysis throughout, I have increased my sample size to a total of 80 samples, and I have also added Sex as a multiple comparison.
htseq-count input
I have imported a total of 80 htseq-count files into a directory, and is displayed in table sampleTable. There are n = 40 from each treatment group in my data file (20 Female, 20 Male for mulitple comparisons).
Creating a variable called ‘directory’ which points to where my output files are located.
directory <- "/Users/maricarmen/Desktop/files"Specifying files that contain “treated” in the filename, and using that to identify the condition status.
sampleFiles <- grep("treated",list.files(directory),value=TRUE)
sampleCondition <- sub("(.*treated).*","\\1",sampleFiles)
sampleType <- sub(".*treated","",sampleFiles)
sampleTable <- data.frame(sampleName = sampleFiles,
fileName = sampleFiles,
condition = sampleCondition,
type = sampleType)
sampleTable$condition <- factor(sampleTable$condition)
sampleTable$type <- factor(sampleTable$type)Building the DESeqDataSet
library("DESeq2")Loading required package: S4Vectors
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Attaching package: ‘BiocGenerics’
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match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which.max, which.min
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colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges,
colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs,
rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs,
rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads,
rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor,
see 'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: ‘Biobase’
The following object is masked from ‘package:MatrixGenerics’:
rowMedians
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anyMissing, rowMediansddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
directory = directory,
design= ~ condition)
ddsHTSeqclass: DESeqDataSet
dim: 60483 80
metadata(1): version
assays(1): counts
rownames(60483): ENSG00000000003.13 ENSG00000000005.5 ... ENSGR0000280767.1 ENSGR0000281849.1
rowData names(0):
colnames(80): treatedF_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts
treatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts ...
untreatedM_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts
untreatedM_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts
colData names(2): condition typePre-filtering
keep <- rowSums(counts(ddsHTSeq)) >= 10
dds <- ddsHTSeq[keep,]Setting factor levels
dds$condition <- factor(dds$condition, levels = c("untreated","treated"))Differential Expression Analysis
dds <- DESeq(dds)estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 4641 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testingres <- results(dds)
res <- results(dds, name="condition_treated_vs_untreated")
reslog2 fold change (MLE): condition treated vs untreated
Wald test p-value: condition treated vs untreated
DataFrame with 40867 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
ENSG00000000003.13 4453.96587 -0.1997879 0.1850133 -1.0798573 0.280206 0.881472
ENSG00000000005.5 4.40493 -0.1424449 0.5439267 -0.2618826 0.793412 0.985091
ENSG00000000419.11 1164.51323 -0.0883110 0.0897444 -0.9840280 0.325102 0.899680
ENSG00000000457.12 689.51298 0.0034354 0.1243260 0.0276322 0.977955 0.997114
ENSG00000000460.15 341.49032 0.4078734 0.2491110 1.6373160 0.101564 0.727068
... ... ... ... ... ... ...
ENSG00000281904.1 0.217062 -0.188144 0.920972 -0.2042882 0.83812829 NA
ENSG00000281909.1 0.260227 -0.038241 0.809728 -0.0472269 0.96233237 NA
ENSG00000281912.1 58.056910 -0.281619 0.255266 -1.1032368 0.26992436 0.875126
ENSG00000281918.1 0.873386 0.729482 0.619838 1.1768906 0.23923919 NA
ENSG00000281920.1 7.075161 0.899532 0.345914 2.6004500 0.00931016 0.333653Log fold change shrinkage
resultsNames(dds)[1] "Intercept" "condition_treated_vs_untreated"resLFC <- lfcShrink(dds, coef="condition_treated_vs_untreated", type="apeglm")using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895resLFClog2 fold change (MAP): condition treated vs untreated
Wald test p-value: condition treated vs untreated
DataFrame with 40867 rows and 5 columns
baseMean log2FoldChange lfcSE pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric>
ENSG00000000003.13 4453.96587 -4.32659e-06 0.00144265 0.280206 0.881472
ENSG00000000005.5 4.40493 -4.79057e-07 0.00144269 0.793412 0.985091
ENSG00000000419.11 1164.51323 -4.51185e-04 0.00148088 0.325102 0.899680
ENSG00000000457.12 689.51298 1.45701e-05 0.00144263 0.977955 0.997114
ENSG00000000460.15 341.49032 -1.30098e-05 0.00144270 0.101564 0.727068
... ... ... ... ... ...
ENSG00000281904.1 0.217062 -3.84902e-07 0.00144269 0.83812829 NA
ENSG00000281909.1 0.260227 -1.54484e-07 0.00144269 0.96233237 NA
ENSG00000281912.1 58.056910 -4.48092e-06 0.00144268 0.26992436 0.875126
ENSG00000281918.1 0.873386 1.98292e-06 0.00144269 0.23923919 NA
ENSG00000281920.1 7.075161 7.41319e-06 0.00144269 0.00931016 0.333653Ordering results table (by p-values)
resOrdered <- res[order(res$pvalue),]summary(res)
out of 40851 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 63, 0.15%
LFC < 0 (down) : 138, 0.34%
outliers [1] : 0, 0%
low counts [2] : 15064, 37%
(mean count < 2)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultsHow many p-values are less than 0.1?
sum(res$padj < 0.1, na.rm=TRUE)[1] 201Adjusting p-value to 0.5
res05 <- results(dds, alpha=0.05)
summary(res05)
out of 40851 with nonzero total read count
adjusted p-value < 0.05
LFC > 0 (up) : 27, 0.066%
LFC < 0 (down) : 68, 0.17%
outliers [1] : 0, 0%
low counts [2] : 13480, 33%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultsHow many p-values are less than 0.5?
sum(res05$padj < 0.05, na.rm=TRUE)[1] 95MAPlot for shrunkin log2 fold changes
plotMA(resLFC, ylim=c(-2,2))
idx <- identify(res$baseMean, res$log2FoldChange)
rownames(res)[idx]character(0)Alternative shrinkage estimators
resultsNames(dds)[1] "Intercept" "condition_treated_vs_untreated"Setting ‘coef = 2’ (this may take awhile, grab a coffee)
resNorm <- lfcShrink(dds, coef=2, type="normal")using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
Reference: https://doi.org/10.1093/bioinformatics/bty895resAsh <- lfcShrink(dds, coef=2, type="ashr")using 'ashr' for LFC shrinkage. If used in published research, please cite:
Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
https://doi.org/10.1093/biostatistics/kxw041resNorm <- lfcShrink(dds, coef=2, type="normal")using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
Reference: https://doi.org/10.1093/bioinformatics/bty895resAsh <- lfcShrink(dds, coef=2, type="ashr")using 'ashr' for LFC shrinkage. If used in published research, please cite:
Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
https://doi.org/10.1093/biostatistics/kxw041par(mfrow=c(1,3), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-3,3)
plotMA(resLFC, xlim=xlim, ylim=ylim, main="apeglm")
plotMA(resNorm, xlim=xlim, ylim=ylim, main="normal")plotMA(resAsh, xlim=xlim, ylim=ylim, main="ashr")
Plot counts
plotCounts(dds, gene=which.min(res$padj), intgroup="condition")
Plotting using ggplot
d <- plotCounts(dds, gene=which.min(res$padj), intgroup="condition",
returnData=TRUE)
library("ggplot2")
ggplot(d, aes(x=condition, y=count)) +
geom_point(position=position_jitter(w=0.1,h=0)) +
scale_y_log10(breaks=c(25,100,400))
Multi-factor design
Our sample population has a condition of interest (treated for liver cancer or untreated), but we also have the Sex of each patient under “type”. By using the colData function, we see that we have to chop/clean up our Type names.
colData(dds)DataFrame with 80 rows and 4 columns
condition
<factor>
treatedF_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts treated
treatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts treated
treatedF_160aee04-df36-4e94-90c5-b01b2991ba48.htseq.counts treated
treatedF_1a228668-b2a0-469f-8801-fdc8be449b44.htseq.counts treated
treatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts treated
... ...
untreatedM_d7a9f4a9-6998-45dc-9bc4-06f3f7110429.htseq.counts untreated
untreatedM_e035a46e-6114-4a64-b5ae-9e6209223493.htseq.counts untreated
untreatedM_e208fa4d-3f65-4615-bd6e-870a52bb6da3.htseq.counts untreated
untreatedM_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts untreated
untreatedM_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts untreated
type
<factor>
treatedF_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts F_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts
treatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts F_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts
treatedF_160aee04-df36-4e94-90c5-b01b2991ba48.htseq.counts F_160aee04-df36-4e94-90c5-b01b2991ba48.htseq.counts
treatedF_1a228668-b2a0-469f-8801-fdc8be449b44.htseq.counts F_1a228668-b2a0-469f-8801-fdc8be449b44.htseq.counts
treatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts F_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts
... ...
untreatedM_d7a9f4a9-6998-45dc-9bc4-06f3f7110429.htseq.counts M_d7a9f4a9-6998-45dc-9bc4-06f3f7110429.htseq.counts
untreatedM_e035a46e-6114-4a64-b5ae-9e6209223493.htseq.counts M_e035a46e-6114-4a64-b5ae-9e6209223493.htseq.counts
untreatedM_e208fa4d-3f65-4615-bd6e-870a52bb6da3.htseq.counts M_e208fa4d-3f65-4615-bd6e-870a52bb6da3.htseq.counts
untreatedM_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts M_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts
untreatedM_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts M_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts
sizeFactor replaceable
<numeric> <logical>
treatedF_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts 0.524686 TRUE
treatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts 0.945973 TRUE
treatedF_160aee04-df36-4e94-90c5-b01b2991ba48.htseq.counts 0.766143 TRUE
treatedF_1a228668-b2a0-469f-8801-fdc8be449b44.htseq.counts 1.999997 TRUE
treatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts 1.271887 TRUE
... ... ...
untreatedM_d7a9f4a9-6998-45dc-9bc4-06f3f7110429.htseq.counts 0.995618 TRUE
untreatedM_e035a46e-6114-4a64-b5ae-9e6209223493.htseq.counts 1.157396 TRUE
untreatedM_e208fa4d-3f65-4615-bd6e-870a52bb6da3.htseq.counts 1.358135 TRUE
untreatedM_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts 0.406704 TRUE
untreatedM_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts 0.653657 TRUEFirst, I create a copy of the DDS so we can run a multi-factor analysis.
ddsMF <- ddsThen, we change how “Type” is displayed for each sample participant.
levels(ddsMF$type) [1] "F_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts"
[2] "F_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts"
[3] "F_104c7c7e-583a-4496-a685-5d277e28c11b.htseq.counts"
[4] "F_10a32136-8a4d-4af3-8e70-543b2a336c91.htseq.counts"
[5] "F_160aee04-df36-4e94-90c5-b01b2991ba48.htseq.counts"
[6] "F_16fc8611-259d-4cd8-9e93-60d97bebb6bf.htseq.counts"
[7] "F_1a228668-b2a0-469f-8801-fdc8be449b44.htseq.counts"
[8] "F_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts"
[9] "F_28ef84e7-f566-40a7-b239-26d3e23c878a.htseq.counts"
[10] "F_2baabd07-b92c-4ac1-8de7-3d8df4f4a80b.htseq.counts"
[11] "F_2f3f2e57-4c69-469f-be9c-01cc4f704427.htseq.counts"
[12] "F_3e0831c0-6726-4f71-9cba-faaeea280cf9.htseq.counts"
[13] "F_554f6de3-63c7-47b1-a75a-dcfc73f54e96.htseq.counts"
[14] "F_6c86f9ae-9e88-4eae-9ba3-4866943cca7f.htseq.counts"
[15] "F_6cd9b90b-0c6c-44bd-8536-5a93553a7730.htseq.counts"
[16] "F_73756e81-79a2-4bff-96e7-99f038306b3e.htseq.counts"
[17] "F_7a3a131b-883d-4f82-8b3b-ede7733f68d8.htseq.counts"
[18] "F_7b77e8cb-3f1d-4af7-b609-d75f912484c4.htseq.counts"
[19] "F_89e20bd9-15e5-48e6-9dfe-0b919c817216.htseq.counts"
[20] "F_8d78e34d-490b-4f2e-ba1a-427afe96d26e.htseq.counts"
[21] "F_92ee9ee1-8f03-4b53-b6c2-04d22fd692ae.htseq.counts"
[22] "F_98380eb7-4de1-4e3a-989c-eedf6eec2977.htseq.counts"
[23] "F_99e82612-a602-459b-8a60-80a812bf9d88.htseq.counts"
[24] "F_9ccd91ba-7180-443a-b03b-f9b398c679e4.htseq.counts"
[25] "F_aaec3912-230f-4418-986d-878e77480f94.htseq.counts"
[26] "F_b39bcb10-164d-48c5-89ba-9ef668765db6.htseq.counts"
[27] "F_b49faa57-5a15-4193-9552-130d9a63eb40.htseq.counts"
[28] "F_d2bf6185-08a5-4a73-9f87-7164cca1ccf6.htseq.counts"
[29] "F_d5fd715a-cf6d-4b29-ad9d-b3e58c55262a.htseq.counts"
[30] "F_edf02d6d-619f-4325-a585-dca7bf5afbeb.htseq.counts"
[31] "F_ee857c4c-e6a9-4f39-b95b-20fe46dd1ab7.htseq.counts"
[32] "F_eea3fcee-ee51-461b-abb4-c393c084e018.htseq.counts"
[33] "F_eed21a5a-30db-45a0-9b60-3f6a91b38cb6.htseq.counts"
[34] "F_f6ae6ac1-3e00-4021-a6e7-fbb0d5f12836.htseq.counts"
[35] "F_f7a956f8-4dc2-437a-92db-8baf8f9b3f14.htseq.counts"
[36] "F_ffeed225-c2a3-4b4c-954c-4816903782a9.htseq.counts"
[37] "M_04e7f1a4-3173-4a6f-af60-04e1f2e29868.htseq.counts"
[38] "M_05ac7b05-e459-4833-97fc-530185a7a55f.htseq.counts"
[39] "M_063411ca-874c-49ff-85c2-b6946f43df4e.htseq.counts"
[40] "M_09f395dd-d007-41e0-8a51-a6533ae51087.htseq.counts"
[41] "M_10d6deec-fd89-4115-92f5-c9def40428b5.htseq.counts"
[42] "M_1c6bb58a-3869-41f5-9fef-9070bbfedbad.htseq.counts"
[43] "M_1d782a72-61f3-4002-bee5-67daeb8244e3.htseq.counts"
[44] "M_2cc2e3ce-68cd-4690-9fff-5ecf86c2f57a.htseq.counts"
[45] "M_35a2858e-9787-457c-9435-33bd62332aa3.htseq.counts"
[46] "M_51c2d807-7ee6-4b42-a6a7-3eb14f987bc0.htseq.counts"
[47] "M_57ef7745-338c-4fb2-ac9b-9f57daba2c51.htseq.counts"
[48] "M_77b9a94e-aaa7-409c-9849-86ba7d5c061a.htseq.counts"
[49] "M_78e27399-eb4a-4b86-992e-3e052bad044c.htseq.counts"
[50] "M_79f4b12a-ed1c-46d5-ae3d-a0422454eaf4.htseq.counts"
[51] "M_7f86ace4-9a17-4b17-8a79-3b2ef53f62c3.htseq.counts"
[52] "M_89eb1fc8-6b38-446e-a4b0-fb9c520298dc.htseq.counts"
[53] "M_9700948e-21f5-42ae-9cb5-f18c745887ce.htseq.counts"
[54] "M_a3a6543b-ea79-4724-a140-c83392b1842b.htseq.counts"
[55] "M_ad69efb1-61ab-4492-8468-9006dd00e915.htseq.counts"
[56] "M_b04fb368-4b93-4ef9-ae9f-020484c1686b.htseq.counts"
[57] "M_b1820867-e6b2-4228-ad00-19d1d7fddf67.htseq.counts"
[58] "M_b5d92dd1-d98b-4b9e-953e-81c04dfb1e0d.htseq.counts"
[59] "M_bdd35388-0850-40ac-a21a-6bfee0c34eef.htseq.counts"
[60] "M_c49c187f-98e7-463d-a18b-accf3205eb81.htseq.counts"
[61] "M_c4c4578a-f1c2-462a-873f-c8db84fe3a6f.htseq.counts"
[62] "M_c7e1465f-cc5a-4dc9-9bb1-6f1112da7aad.htseq.counts"
[63] "M_cfb40dba-37a7-4ae7-a2c9-34becc30f939.htseq.counts"
[64] "M_d1cbb686-93d8-4349-8bf5-756887c60ebc.htseq.counts"
[65] "M_d7a9f4a9-6998-45dc-9bc4-06f3f7110429.htseq.counts"
[66] "M_dad3353e-6721-4e2e-ae06-04eea4c00f12.htseq.counts"
[67] "M_dcf5a350-7cff-475a-8f53-376422f990eb.htseq.counts"
[68] "M_e035a46e-6114-4a64-b5ae-9e6209223493.htseq.counts"
[69] "M_e208fa4d-3f65-4615-bd6e-870a52bb6da3.htseq.counts"
[70] "M_e48c520a-d62c-440d-b17c-5b5ff03d41a2.htseq.counts"
[71] "M_e4d48564-e021-4bbc-bc00-145e9d64b501.htseq.counts"
[72] "M_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts"
[73] "M_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts"We will chop/clean up “Type” so that it only specifies F for female patient or M for male patient.
levels(ddsMF$type) <- sub("_.*", "", levels(ddsMF$type))
levels(ddsMF$type)[1] "F" "M"As condition is the variable of interest, we put it at the end of our formula. We then re-run DeSeq analysis:
design(ddsMF) <- formula(~ type + condition)
ddsMF <- DESeq(ddsMF)using pre-existing size factors
estimating dispersions
found already estimated dispersions, replacing these
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 4721 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testingWe access results using the results function:
resMF <- results(ddsMF)
head(resMF)log2 fold change (MLE): condition treated vs untreated
Wald test p-value: condition treated vs untreated
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
ENSG00000000003.13 4453.96587 -0.2138352 0.1814214 -1.178666 0.238531 0.838756
ENSG00000000005.5 4.40493 -0.1681652 0.5402803 -0.311256 0.755606 0.974777
ENSG00000000419.11 1164.51323 -0.0888392 0.0901364 -0.985609 0.324325 0.878919
ENSG00000000457.12 689.51298 0.0206650 0.1233242 0.167567 0.866924 0.988850
ENSG00000000460.15 341.49032 0.3933651 0.2501419 1.572568 0.115819 0.716756
ENSG00000000938.11 272.32558 0.0680974 0.2307494 0.295114 0.767907 0.976718Now we use the contrast function to extract log2 fold changes while specifying Type
resMFType <- results(ddsMF,
contrast=c("type", "F", "M"))
head(resMFType)log2 fold change (MLE): type F vs M
Wald test p-value: type F vs M
DataFrame with 6 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
ENSG00000000003.13 4453.96587 -0.3789700 0.1814214 -2.088894 0.0367173 0.1511632
ENSG00000000005.5 4.40493 0.8600421 0.5405167 1.591148 0.1115763 0.2963857
ENSG00000000419.11 1164.51323 0.0383597 0.0901364 0.425573 0.6704187 0.8258209
ENSG00000000457.12 689.51298 0.1835394 0.1233242 1.488267 0.1366805 0.3335258
ENSG00000000460.15 341.49032 -0.0914826 0.2501418 -0.365723 0.7145719 0.8516444
ENSG00000000938.11 272.32558 0.5433143 0.2307498 2.354561 0.0185446 0.0985477Info on results columns
mcols(res)$description[1] "mean of normalized counts for all samples"
[2] "log2 fold change (MLE): condition treated vs untreated"
[3] "standard error: condition treated vs untreated"
[4] "Wald statistic: condition treated vs untreated"
[5] "Wald test p-value: condition treated vs untreated"
[6] "BH adjusted p-values" Exporting to CSV file (when ran, this will expor CSV to your Desktop)
write.csv(as.data.frame(resOrdered),
file="condition_treated_results.csv")Data transformations and visualizations!
Extracting transformed values (note: this takes a VERY long time to run, take a break go get a coffee)
vsd <- vst(dds, blind=FALSE)
rld <- rlog(dds, blind=FALSE)rlog() may take a long time with 50 or more samples,
vst() is a much faster transformationhead(assay(vsd), 3) treatedF_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts
ENSG00000000003.13 12.010108
ENSG00000000005.5 3.803133
ENSG00000000419.11 9.624601
treatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts
ENSG00000000003.13 12.000345
ENSG00000000005.5 3.837895
ENSG00000000419.11 10.065377
treatedF_160aee04-df36-4e94-90c5-b01b2991ba48.htseq.counts
ENSG00000000003.13 12.133125
ENSG00000000005.5 4.344518
ENSG00000000419.11 10.088965
treatedF_1a228668-b2a0-469f-8801-fdc8be449b44.htseq.counts
ENSG00000000003.13 12.126734
ENSG00000000005.5 3.480745
ENSG00000000419.11 10.358151
treatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts
ENSG00000000003.13 11.335300
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.327496
treatedF_28ef84e7-f566-40a7-b239-26d3e23c878a.htseq.counts
ENSG00000000003.13 9.669128
ENSG00000000005.5 3.677303
ENSG00000000419.11 9.877421
treatedF_2baabd07-b92c-4ac1-8de7-3d8df4f4a80b.htseq.counts
ENSG00000000003.13 11.031863
ENSG00000000005.5 3.776332
ENSG00000000419.11 10.265872
treatedF_3e0831c0-6726-4f71-9cba-faaeea280cf9.htseq.counts
ENSG00000000003.13 11.382912
ENSG00000000005.5 3.724349
ENSG00000000419.11 10.583432
treatedF_554f6de3-63c7-47b1-a75a-dcfc73f54e96.htseq.counts
ENSG00000000003.13 11.690293
ENSG00000000005.5 3.137687
ENSG00000000419.11 11.313958
treatedF_6cd9b90b-0c6c-44bd-8536-5a93553a7730.htseq.counts
ENSG00000000003.13 12.533113
ENSG00000000005.5 4.553765
ENSG00000000419.11 10.464822
treatedF_7a3a131b-883d-4f82-8b3b-ede7733f68d8.htseq.counts
ENSG00000000003.13 12.011736
ENSG00000000005.5 3.786213
ENSG00000000419.11 10.395398
treatedF_7b77e8cb-3f1d-4af7-b609-d75f912484c4.htseq.counts
ENSG00000000003.13 10.259599
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.829390
treatedF_89e20bd9-15e5-48e6-9dfe-0b919c817216.htseq.counts
ENSG00000000003.13 10.670326
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.485302
treatedF_98380eb7-4de1-4e3a-989c-eedf6eec2977.htseq.counts
ENSG00000000003.13 12.086947
ENSG00000000005.5 6.089224
ENSG00000000419.11 10.167475
treatedF_9ccd91ba-7180-443a-b03b-f9b398c679e4.htseq.counts
ENSG00000000003.13 12.120421
ENSG00000000005.5 4.456754
ENSG00000000419.11 10.003843
treatedF_aaec3912-230f-4418-986d-878e77480f94.htseq.counts
ENSG00000000003.13 12.558911
ENSG00000000005.5 4.416152
ENSG00000000419.11 10.712649
treatedF_d5fd715a-cf6d-4b29-ad9d-b3e58c55262a.htseq.counts
ENSG00000000003.13 11.694173
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.941086
treatedF_ee857c4c-e6a9-4f39-b95b-20fe46dd1ab7.htseq.counts
ENSG00000000003.13 12.068945
ENSG00000000005.5 4.714298
ENSG00000000419.11 10.060595
treatedF_eea3fcee-ee51-461b-abb4-c393c084e018.htseq.counts
ENSG00000000003.13 11.723174
ENSG00000000005.5 4.270978
ENSG00000000419.11 9.465239
treatedF_f6ae6ac1-3e00-4021-a6e7-fbb0d5f12836.htseq.counts
ENSG00000000003.13 10.984981
ENSG00000000005.5 3.541637
ENSG00000000419.11 9.541770
treatedM_04e7f1a4-3173-4a6f-af60-04e1f2e29868.htseq.counts
ENSG00000000003.13 11.423792
ENSG00000000005.5 3.572102
ENSG00000000419.11 10.055809
treatedM_05ac7b05-e459-4833-97fc-530185a7a55f.htseq.counts
ENSG00000000003.13 13.510874
ENSG00000000005.5 3.776884
ENSG00000000419.11 9.737111
treatedM_063411ca-874c-49ff-85c2-b6946f43df4e.htseq.counts
ENSG00000000003.13 13.225876
ENSG00000000005.5 3.860305
ENSG00000000419.11 10.978271
treatedM_1c6bb58a-3869-41f5-9fef-9070bbfedbad.htseq.counts
ENSG00000000003.13 10.819752
ENSG00000000005.5 4.079125
ENSG00000000419.11 10.276796
treatedM_51c2d807-7ee6-4b42-a6a7-3eb14f987bc0.htseq.counts
ENSG00000000003.13 11.625170
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.747406
treatedM_57ef7745-338c-4fb2-ac9b-9f57daba2c51.htseq.counts
ENSG00000000003.13 12.875045
ENSG00000000005.5 4.597585
ENSG00000000419.11 10.166994
treatedM_77b9a94e-aaa7-409c-9849-86ba7d5c061a.htseq.counts
ENSG00000000003.13 11.338110
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.763866
treatedM_79f4b12a-ed1c-46d5-ae3d-a0422454eaf4.htseq.counts
ENSG00000000003.13 11.247954
ENSG00000000005.5 3.583671
ENSG00000000419.11 9.949941
treatedM_7f86ace4-9a17-4b17-8a79-3b2ef53f62c3.htseq.counts
ENSG00000000003.13 11.281103
ENSG00000000005.5 3.823859
ENSG00000000419.11 9.770897
treatedM_9700948e-21f5-42ae-9cb5-f18c745887ce.htseq.counts
ENSG00000000003.13 12.448792
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.848389
treatedM_a3a6543b-ea79-4724-a140-c83392b1842b.htseq.counts
ENSG00000000003.13 12.15704
ENSG00000000005.5 3.70595
ENSG00000000419.11 10.27649
treatedM_b5d92dd1-d98b-4b9e-953e-81c04dfb1e0d.htseq.counts
ENSG00000000003.13 11.642861
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.132197
treatedM_bdd35388-0850-40ac-a21a-6bfee0c34eef.htseq.counts
ENSG00000000003.13 11.690043
ENSG00000000005.5 4.143037
ENSG00000000419.11 10.299080
treatedM_c49c187f-98e7-463d-a18b-accf3205eb81.htseq.counts
ENSG00000000003.13 12.425289
ENSG00000000005.5 4.843572
ENSG00000000419.11 10.249863
treatedM_c4c4578a-f1c2-462a-873f-c8db84fe3a6f.htseq.counts
ENSG00000000003.13 11.523250
ENSG00000000005.5 3.897876
ENSG00000000419.11 9.993003
treatedM_c7e1465f-cc5a-4dc9-9bb1-6f1112da7aad.htseq.counts
ENSG00000000003.13 11.443496
ENSG00000000005.5 4.585825
ENSG00000000419.11 10.471124
treatedM_dad3353e-6721-4e2e-ae06-04eea4c00f12.htseq.counts
ENSG00000000003.13 11.551448
ENSG00000000005.5 3.714484
ENSG00000000419.11 10.152403
treatedM_dcf5a350-7cff-475a-8f53-376422f990eb.htseq.counts
ENSG00000000003.13 12.082464
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.578542
treatedM_e48c520a-d62c-440d-b17c-5b5ff03d41a2.htseq.counts
ENSG00000000003.13 13.902529
ENSG00000000005.5 3.610941
ENSG00000000419.11 10.094763
treatedM_e4d48564-e021-4bbc-bc00-145e9d64b501.htseq.counts
ENSG00000000003.13 11.341795
ENSG00000000005.5 3.564775
ENSG00000000419.11 10.217635
untreatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts
ENSG00000000003.13 12.000345
ENSG00000000005.5 3.837895
ENSG00000000419.11 10.065377
untreatedF_104c7c7e-583a-4496-a685-5d277e28c11b.htseq.counts
ENSG00000000003.13 12.182466
ENSG00000000005.5 3.137687
ENSG00000000419.11 11.221385
untreatedF_10a32136-8a4d-4af3-8e70-543b2a336c91.htseq.counts
ENSG00000000003.13 11.952164
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.266446
untreatedF_16fc8611-259d-4cd8-9e93-60d97bebb6bf.htseq.counts
ENSG00000000003.13 12.141786
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.424273
untreatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts
ENSG00000000003.13 11.335300
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.327496
untreatedF_2f3f2e57-4c69-469f-be9c-01cc4f704427.htseq.counts
ENSG00000000003.13 12.671569
ENSG00000000005.5 4.177815
ENSG00000000419.11 10.310761
untreatedF_3e0831c0-6726-4f71-9cba-faaeea280cf9.htseq.counts
ENSG00000000003.13 11.382912
ENSG00000000005.5 3.724349
ENSG00000000419.11 10.583432
untreatedF_6c86f9ae-9e88-4eae-9ba3-4866943cca7f.htseq.counts
ENSG00000000003.13 12.413538
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.151889
untreatedF_73756e81-79a2-4bff-96e7-99f038306b3e.htseq.counts
ENSG00000000003.13 12.522536
ENSG00000000005.5 3.874868
ENSG00000000419.11 10.174565
untreatedF_8d78e34d-490b-4f2e-ba1a-427afe96d26e.htseq.counts
ENSG00000000003.13 11.897977
ENSG00000000005.5 3.595631
ENSG00000000419.11 9.494392
untreatedF_92ee9ee1-8f03-4b53-b6c2-04d22fd692ae.htseq.counts
ENSG00000000003.13 9.749285
ENSG00000000005.5 5.058470
ENSG00000000419.11 10.549118
untreatedF_98380eb7-4de1-4e3a-989c-eedf6eec2977.htseq.counts
ENSG00000000003.13 12.086947
ENSG00000000005.5 6.089224
ENSG00000000419.11 10.167475
untreatedF_99e82612-a602-459b-8a60-80a812bf9d88.htseq.counts
ENSG00000000003.13 11.342236
ENSG00000000005.5 5.220944
ENSG00000000419.11 10.238865
untreatedF_b39bcb10-164d-48c5-89ba-9ef668765db6.htseq.counts
ENSG00000000003.13 12.901341
ENSG00000000005.5 4.440781
ENSG00000000419.11 10.177789
untreatedF_b49faa57-5a15-4193-9552-130d9a63eb40.htseq.counts
ENSG00000000003.13 11.730460
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.949948
untreatedF_d2bf6185-08a5-4a73-9f87-7164cca1ccf6.htseq.counts
ENSG00000000003.13 13.229894
ENSG00000000005.5 3.952792
ENSG00000000419.11 9.967647
untreatedF_edf02d6d-619f-4325-a585-dca7bf5afbeb.htseq.counts
ENSG00000000003.13 12.058079
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.150240
untreatedF_eed21a5a-30db-45a0-9b60-3f6a91b38cb6.htseq.counts
ENSG00000000003.13 7.528241
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.025289
untreatedF_f7a956f8-4dc2-437a-92db-8baf8f9b3f14.htseq.counts
ENSG00000000003.13 12.612583
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.672198
untreatedF_ffeed225-c2a3-4b4c-954c-4816903782a9.htseq.counts
ENSG00000000003.13 11.700559
ENSG00000000005.5 3.697129
ENSG00000000419.11 10.189830
untreatedM_09f395dd-d007-41e0-8a51-a6533ae51087.htseq.counts
ENSG00000000003.13 13.140656
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.268505
untreatedM_10d6deec-fd89-4115-92f5-c9def40428b5.htseq.counts
ENSG00000000003.13 12.800665
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.260551
untreatedM_1d782a72-61f3-4002-bee5-67daeb8244e3.htseq.counts
ENSG00000000003.13 13.627063
ENSG00000000005.5 5.388573
ENSG00000000419.11 10.837103
untreatedM_2cc2e3ce-68cd-4690-9fff-5ecf86c2f57a.htseq.counts
ENSG00000000003.13 11.88208
ENSG00000000005.5 4.82894
ENSG00000000419.11 10.26352
untreatedM_35a2858e-9787-457c-9435-33bd62332aa3.htseq.counts
ENSG00000000003.13 10.86300
ENSG00000000005.5 4.02441
ENSG00000000419.11 10.10995
untreatedM_78e27399-eb4a-4b86-992e-3e052bad044c.htseq.counts
ENSG00000000003.13 11.439581
ENSG00000000005.5 3.765704
ENSG00000000419.11 9.705612
untreatedM_89eb1fc8-6b38-446e-a4b0-fb9c520298dc.htseq.counts
ENSG00000000003.13 12.639202
ENSG00000000005.5 3.622842
ENSG00000000419.11 10.615463
untreatedM_9700948e-21f5-42ae-9cb5-f18c745887ce.htseq.counts
ENSG00000000003.13 12.448792
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.848389
untreatedM_a3a6543b-ea79-4724-a140-c83392b1842b.htseq.counts
ENSG00000000003.13 12.15704
ENSG00000000005.5 3.70595
ENSG00000000419.11 10.27649
untreatedM_ad69efb1-61ab-4492-8468-9006dd00e915.htseq.counts
ENSG00000000003.13 11.240405
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.093893
untreatedM_b04fb368-4b93-4ef9-ae9f-020484c1686b.htseq.counts
ENSG00000000003.13 11.950998
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.683769
untreatedM_b1820867-e6b2-4228-ad00-19d1d7fddf67.htseq.counts
ENSG00000000003.13 12.825720
ENSG00000000005.5 4.104038
ENSG00000000419.11 10.936106
untreatedM_bdd35388-0850-40ac-a21a-6bfee0c34eef.htseq.counts
ENSG00000000003.13 11.690043
ENSG00000000005.5 4.143037
ENSG00000000419.11 10.299080
untreatedM_cfb40dba-37a7-4ae7-a2c9-34becc30f939.htseq.counts
ENSG00000000003.13 11.807158
ENSG00000000005.5 3.137687
ENSG00000000419.11 9.561400
untreatedM_d1cbb686-93d8-4349-8bf5-756887c60ebc.htseq.counts
ENSG00000000003.13 12.862365
ENSG00000000005.5 3.137687
ENSG00000000419.11 10.587888
untreatedM_d7a9f4a9-6998-45dc-9bc4-06f3f7110429.htseq.counts
ENSG00000000003.13 12.157830
ENSG00000000005.5 3.820541
ENSG00000000419.11 9.799839
untreatedM_e035a46e-6114-4a64-b5ae-9e6209223493.htseq.counts
ENSG00000000003.13 11.865715
ENSG00000000005.5 3.771832
ENSG00000000419.11 10.103464
untreatedM_e208fa4d-3f65-4615-bd6e-870a52bb6da3.htseq.counts
ENSG00000000003.13 12.020334
ENSG00000000005.5 3.553532
ENSG00000000419.11 10.590308
untreatedM_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts
ENSG00000000003.13 12.779825
ENSG00000000005.5 4.192442
ENSG00000000419.11 10.385769
untreatedM_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts
ENSG00000000003.13 11.681830
ENSG00000000005.5 4.158100
ENSG00000000419.11 9.794419Effects on transformation on variance (Standard Deviation) for the normal logarithm transformation (ntd)
# this gives log2(n + 1)
ntd <- normTransform(dds)
library("vsn")
meanSdPlot(assay(ntd))
Effects on transformation on variance (Standard Deviation) for the variance stabilizing transformation (vsd)
meanSdPlot(assay(vsd))
Effects on transformation on variance (Standard Deviation) for the regularized log transformation (rld)
meanSdPlot(assay(rld))
Heatmap of count matrix
Known issue: I had to remove colnames because they were too complicated, and the heatmap wasn’t being displayed. Will need to come back to this. Other known issue: Not sure why female and male are not clumped together, I think this has to do with how my files are ordered. I need to come back to this.
This is a heatmap with both condition and type being analyzed. Multi-factor design.
library("pheatmap")
select <- order(rowMeans(counts(ddsMF,normalized=TRUE)),
decreasing=TRUE)[1:20]
df <- as.data.frame(colData(ddsMF)[,c("condition","type")])
pheatmap(assay(ntd)[select,], cluster_rows=FALSE, show_rownames=FALSE, show_colnames = FALSE,
cluster_cols=FALSE, annotation_col=df)
Heatmap using ntd data described above.
library("pheatmap")
select <- order(rowMeans(counts(dds,normalized=TRUE)),
decreasing=TRUE)[1:20]
df <- as.data.frame(colData(ddsMF)[,c("condition","type")])
pheatmap(assay(ntd)[select,], cluster_rows=FALSE, show_rownames=FALSE, show_colnames = FALSE,
cluster_cols=FALSE, annotation_col=df)
Heatmap using vsd data described above.
pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=FALSE, show_colnames = FALSE,
cluster_cols=FALSE, annotation_col=df)
Heatmap using rld data described above.
pheatmap(assay(rld)[select,], cluster_rows=FALSE, show_rownames=FALSE, show_colnames = FALSE,
cluster_cols=FALSE, annotation_col=df)
Sample clustering
sampleDists <- dist(t(assay(vsd)))Creating a heatmap of the sample-to-sample distances.
library("pheatmap")
library("RColorBrewer")
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$condition)
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
clustering_distance_rows=sampleDists,
clustering_distance_cols=sampleDists,
col=colors)
PCA plot using vsd
plotPCA(vsd, intgroup="condition", ntop = 500, returnData = FALSE)
Variations to the standard workflow!
Performing Wald test on dataset
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)found already estimated dispersions, replacing these
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimatesdds <- nbinomWaldTest(dds)resultsNames(dds)[1] "Intercept" "condition_treated_vs_untreated"Performing Liklihood ratio test on dataset
dds <- DESeq(dds, test="LRT", reduced=~1)using pre-existing size factors
estimating dispersions
found already estimated dispersions, replacing these
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 4685 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testingres <- results(dds)False sign or small rate in MA-plot
resApeT <- lfcShrink(dds, coef=2, type="apeglm", lfcThreshold=1)using 'apeglm' for LFC shrinkage. If used in published research, please cite:
Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
sequence count data: removing the noise and preserving large differences.
Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
computing FSOS 'false sign or small' s-values (T=1)plotMA(resApeT, ylim=c(-3,3), cex=.8)thresholding s-values on alpha=0.005 to color pointsabline(h=c(-1,1), col="dodgerblue", lwd=2)
summary(res)
out of 40852 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 42, 0.1%
LFC < 0 (down) : 105, 0.26%
outliers [1] : 0, 0%
low counts [2] : 15855, 39%
(mean count < 2)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?resultsDispersion plot and fitting alternatives!
Dispersion plot
plotDispEsts(dds)
Custom dispersion fit
ddsCustom <- dds
useForMedian <- mcols(ddsCustom)$dispGeneEst > 1e-7
medianDisp <- median(mcols(ddsCustom)$dispGeneEst[useForMedian],
na.rm=TRUE)
dispersionFunction(ddsCustom) <- function(mu) medianDisp
ddsCustom <- estimateDispersionsMAP(ddsCustom)found already estimated dispersions, removing theseIndependent filtering of results
metadata(res)$alpha[1] 0.1metadata(res)$filterThreshold38.79723%
2.252725 Supplying a custom dispersion fit
ddsCustom <- dds
useForMedian <- mcols(ddsCustom)$dispGeneEst > 1e-7
medianDisp <- median(mcols(ddsCustom)$dispGeneEst[useForMedian],
na.rm=TRUE)
dispersionFunction(ddsCustom) <- function(mu) medianDisp
ddsCustom <- estimateDispersionsMAP(ddsCustom)found already estimated dispersions, removing theseIndependent filtering of results
plot(metadata(res)$filterNumRej,
type="b", ylab="number of rejections",
xlab="quantiles of filter")
lines(metadata(res)$lo.fit, col="red")abline(v=metadata(res)$filterTheta)
Turn off independent filtering
resNoFilt <- results(dds, independentFiltering=FALSE)
addmargins(table(filtering=(res$padj < .1),
noFiltering=(resNoFilt$padj < .1))) noFiltering
filtering FALSE TRUE Sum
FALSE 24865 0 24865
TRUE 66 81 147
Sum 24931 81 250124 possible alternative hypotheses in MA-plot (note: takes long time to run, go grab a coffee. Also, make sure to turn off independent filtering, and reload/rerun Wald test)
par(mfrow=c(2,2),mar=c(2,2,1,1))
ylim <- c(-2.5,2.5)
resGA <- results(dds, lfcThreshold=.5, altHypothesis="greaterAbs")
resLA <- results(dds, lfcThreshold=.5, altHypothesis="lessAbs")
resG <- results(dds, lfcThreshold=.5, altHypothesis="greater")
resL <- results(dds, lfcThreshold=.5, altHypothesis="less")
drawLines <- function() abline(h=c(-.5,.5),col="dodgerblue",lwd=2)
plotMA(resGA, ylim=ylim); drawLines()
plotMA(resLA, ylim=ylim); drawLines()plotMA(resG, ylim=ylim); drawLines()
plotMA(resL, ylim=ylim); drawLines()
Access to all calculated values
mcols(dds,use.names=TRUE)[1:4,1:4]DataFrame with 4 rows and 4 columns
baseMean baseVar allZero dispGeneEst
<numeric> <numeric> <logical> <numeric>
ENSG00000000003.13 4453.96587 6.80235e+06 FALSE 0.3426609
ENSG00000000005.5 4.40493 8.25872e+01 FALSE 2.6284098
ENSG00000000419.11 1164.51323 1.16676e+05 FALSE 0.0724843
ENSG00000000457.12 689.51298 7.08616e+04 FALSE 0.1475156substr(names(mcols(dds)),1,10) [1] "baseMean" "baseVar" "allZero" "dispGeneEs" "dispGeneIt" "dispFit" "dispersion" "dispIter"
[9] "dispOutlie" "dispMAP" "Intercept" "condition_" "SE_Interce" "SE_conditi" "WaldStatis" "WaldStatis"
[17] "WaldPvalue" "WaldPvalue" "betaConv" "betaIter" "deviance" "maxCooks" mcols(mcols(dds), use.names=TRUE)[1:4,]DataFrame with 4 rows and 2 columns
type description
<character> <character>
baseMean intermediate mean of normalized c..
baseVar intermediate variance of normaliz..
allZero intermediate all counts for a gen..
dispGeneEst intermediate gene-wise estimates ..Mean values and Cook’s distances
head(assays(dds)[["mu"]]) treatedF_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts
ENSG00000000003.13 2175.258337
ENSG00000000005.5 2.179508
ENSG00000000419.11 592.427923
ENSG00000000457.12 362.263636
ENSG00000000460.15 204.425909
ENSG00000000938.11 148.032876
treatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts
ENSG00000000003.13 3921.840178
ENSG00000000005.5 3.929502
ENSG00000000419.11 1068.106529
ENSG00000000457.12 653.136255
ENSG00000000460.15 368.565761
ENSG00000000938.11 266.893026
treatedF_160aee04-df36-4e94-90c5-b01b2991ba48.htseq.counts
ENSG00000000003.13 3176.2943
ENSG00000000005.5 3.1825
ENSG00000000419.11 865.0584
ENSG00000000457.12 528.9744
ENSG00000000460.15 298.5010
ENSG00000000938.11 216.1564
treatedF_1a228668-b2a0-469f-8801-fdc8be449b44.htseq.counts
ENSG00000000003.13 8291.641686
ENSG00000000005.5 8.307841
ENSG00000000419.11 2258.214567
ENSG00000000457.12 1380.875189
ENSG00000000460.15 779.229924
ENSG00000000938.11 564.271169
treatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts
ENSG00000000003.13 5273.023466
ENSG00000000005.5 5.283325
ENSG00000000419.11 1436.099009
ENSG00000000457.12 878.159905
ENSG00000000460.15 495.546941
ENSG00000000938.11 358.845115
treatedF_28ef84e7-f566-40a7-b239-26d3e23c878a.htseq.counts
ENSG00000000003.13 6656.17608
ENSG00000000005.5 6.66918
ENSG00000000419.11 1812.79828
ENSG00000000457.12 1108.50767
ENSG00000000460.15 625.53253
ENSG00000000938.11 452.97281
treatedF_2baabd07-b92c-4ac1-8de7-3d8df4f4a80b.htseq.counts
ENSG00000000003.13 7094.851646
ENSG00000000005.5 7.108713
ENSG00000000419.11 1932.270827
ENSG00000000457.12 1181.563914
ENSG00000000460.15 666.758275
ENSG00000000938.11 482.826005
treatedF_3e0831c0-6726-4f71-9cba-faaeea280cf9.htseq.counts
ENSG00000000003.13 5619.53140
ENSG00000000005.5 5.63051
ENSG00000000419.11 1530.46986
ENSG00000000457.12 935.86672
ENSG00000000460.15 528.11098
ENSG00000000938.11 382.42602
treatedF_554f6de3-63c7-47b1-a75a-dcfc73f54e96.htseq.counts
ENSG00000000003.13 4540.472811
ENSG00000000005.5 4.549344
ENSG00000000419.11 1236.590079
ENSG00000000457.12 756.162228
ENSG00000000460.15 426.703470
ENSG00000000938.11 308.992839
treatedF_6cd9b90b-0c6c-44bd-8536-5a93553a7730.htseq.counts
ENSG00000000003.13 3160.483427
ENSG00000000005.5 3.166658
ENSG00000000419.11 860.752308
ENSG00000000457.12 526.341262
ENSG00000000460.15 297.015157
ENSG00000000938.11 215.080408
treatedF_7a3a131b-883d-4f82-8b3b-ede7733f68d8.htseq.counts
ENSG00000000003.13 4584.545402
ENSG00000000005.5 4.593502
ENSG00000000419.11 1248.593174
ENSG00000000457.12 763.501998
ENSG00000000460.15 430.845314
ENSG00000000938.11 311.992111
treatedF_7b77e8cb-3f1d-4af7-b609-d75f912484c4.htseq.counts
ENSG00000000003.13 6597.84003
ENSG00000000005.5 6.61073
ENSG00000000419.11 1796.91056
ENSG00000000457.12 1098.79249
ENSG00000000460.15 620.05024
ENSG00000000938.11 449.00287
treatedF_89e20bd9-15e5-48e6-9dfe-0b919c817216.htseq.counts
ENSG00000000003.13 4677.991367
ENSG00000000005.5 4.687131
ENSG00000000419.11 1274.043024
ENSG00000000457.12 779.064322
ENSG00000000460.15 439.627156
ENSG00000000938.11 318.351390
treatedF_98380eb7-4de1-4e3a-989c-eedf6eec2977.htseq.counts
ENSG00000000003.13 4015.830234
ENSG00000000005.5 4.023676
ENSG00000000419.11 1093.704562
ENSG00000000457.12 668.789191
ENSG00000000460.15 377.398736
ENSG00000000938.11 273.289332
treatedF_9ccd91ba-7180-443a-b03b-f9b398c679e4.htseq.counts
ENSG00000000003.13 2103.303007
ENSG00000000005.5 2.107412
ENSG00000000419.11 572.831011
ENSG00000000457.12 350.280324
ENSG00000000460.15 197.663708
ENSG00000000938.11 143.136099
treatedF_aaec3912-230f-4418-986d-878e77480f94.htseq.counts
ENSG00000000003.13 3934.649572
ENSG00000000005.5 3.942337
ENSG00000000419.11 1071.595146
ENSG00000000457.12 655.269508
ENSG00000000460.15 369.769559
ENSG00000000938.11 267.764744
treatedF_d5fd715a-cf6d-4b29-ad9d-b3e58c55262a.htseq.counts
ENSG00000000003.13 4680.411947
ENSG00000000005.5 4.689556
ENSG00000000419.11 1274.702265
ENSG00000000457.12 779.467442
ENSG00000000460.15 439.854637
ENSG00000000938.11 318.516118
treatedF_ee857c4c-e6a9-4f39-b95b-20fe46dd1ab7.htseq.counts
ENSG00000000003.13 3216.739634
ENSG00000000005.5 3.223024
ENSG00000000419.11 876.073590
ENSG00000000457.12 535.710070
ENSG00000000460.15 302.301990
ENSG00000000938.11 218.908812
treatedF_eea3fcee-ee51-461b-abb4-c393c084e018.htseq.counts
ENSG00000000003.13 3626.52501
ENSG00000000005.5 3.63361
ENSG00000000419.11 987.67794
ENSG00000000457.12 603.95499
ENSG00000000460.15 340.81270
ENSG00000000938.11 246.79594
treatedF_f6ae6ac1-3e00-4021-a6e7-fbb0d5f12836.htseq.counts
ENSG00000000003.13 5969.399371
ENSG00000000005.5 5.981062
ENSG00000000419.11 1625.755807
ENSG00000000457.12 994.133104
ENSG00000000460.15 560.990790
ENSG00000000938.11 406.235591
treatedM_04e7f1a4-3173-4a6f-af60-04e1f2e29868.htseq.counts
ENSG00000000003.13 5156.248498
ENSG00000000005.5 5.166322
ENSG00000000419.11 1404.295544
ENSG00000000457.12 858.712410
ENSG00000000460.15 484.572691
ENSG00000000938.11 350.898227
treatedM_05ac7b05-e459-4833-97fc-530185a7a55f.htseq.counts
ENSG00000000003.13 2360.802120
ENSG00000000005.5 2.365414
ENSG00000000419.11 642.960458
ENSG00000000457.12 393.163766
ENSG00000000460.15 221.862898
ENSG00000000938.11 160.659689
treatedM_063411ca-874c-49ff-85c2-b6946f43df4e.htseq.counts
ENSG00000000003.13 7355.353250
ENSG00000000005.5 7.369723
ENSG00000000419.11 2003.217998
ENSG00000000457.12 1224.947386
ENSG00000000460.15 691.239633
ENSG00000000938.11 500.553923
treatedM_1c6bb58a-3869-41f5-9fef-9070bbfedbad.htseq.counts
ENSG00000000003.13 4271.120665
ENSG00000000005.5 4.279465
ENSG00000000419.11 1163.232478
ENSG00000000457.12 711.304803
ENSG00000000460.15 401.390359
ENSG00000000938.11 290.662614
treatedM_51c2d807-7ee6-4b42-a6a7-3eb14f987bc0.htseq.counts
ENSG00000000003.13 3490.883259
ENSG00000000005.5 3.497703
ENSG00000000419.11 950.736142
ENSG00000000457.12 581.365460
ENSG00000000460.15 328.065394
ENSG00000000938.11 237.565111
treatedM_57ef7745-338c-4fb2-ac9b-9f57daba2c51.htseq.counts
ENSG00000000003.13 2958.87940
ENSG00000000005.5 2.96466
ENSG00000000419.11 805.84579
ENSG00000000457.12 492.76649
ENSG00000000460.15 278.06886
ENSG00000000938.11 201.36065
treatedM_77b9a94e-aaa7-409c-9849-86ba7d5c061a.htseq.counts
ENSG00000000003.13 4764.475611
ENSG00000000005.5 4.773784
ENSG00000000419.11 1297.596862
ENSG00000000457.12 793.467254
ENSG00000000460.15 447.754752
ENSG00000000938.11 324.236903
treatedM_79f4b12a-ed1c-46d5-ae3d-a0422454eaf4.htseq.counts
ENSG00000000003.13 4890.222459
ENSG00000000005.5 4.899776
ENSG00000000419.11 1331.843803
ENSG00000000457.12 814.408909
ENSG00000000460.15 459.572159
ENSG00000000938.11 332.794355
treatedM_7f86ace4-9a17-4b17-8a79-3b2ef53f62c3.htseq.counts
ENSG00000000003.13 2043.549470
ENSG00000000005.5 2.047542
ENSG00000000419.11 556.557236
ENSG00000000457.12 340.329077
ENSG00000000460.15 192.048205
ENSG00000000938.11 139.069691
treatedM_9700948e-21f5-42ae-9cb5-f18c745887ce.htseq.counts
ENSG00000000003.13 3186.161218
ENSG00000000005.5 3.192386
ENSG00000000419.11 867.745610
ENSG00000000457.12 530.617595
ENSG00000000460.15 299.428299
ENSG00000000938.11 216.827859
treatedM_a3a6543b-ea79-4724-a140-c83392b1842b.htseq.counts
ENSG00000000003.13 2997.201091
ENSG00000000005.5 3.003057
ENSG00000000419.11 816.282640
ENSG00000000457.12 499.148513
ENSG00000000460.15 281.670249
ENSG00000000938.11 203.968554
treatedM_b5d92dd1-d98b-4b9e-953e-81c04dfb1e0d.htseq.counts
ENSG00000000003.13 6931.404510
ENSG00000000005.5 6.944946
ENSG00000000419.11 1887.756277
ENSG00000000457.12 1154.343720
ENSG00000000460.15 651.397879
ENSG00000000938.11 471.702936
treatedM_bdd35388-0850-40ac-a21a-6bfee0c34eef.htseq.counts
ENSG00000000003.13 4658.579772
ENSG00000000005.5 4.667681
ENSG00000000419.11 1268.756309
ENSG00000000457.12 775.831550
ENSG00000000460.15 437.802898
ENSG00000000938.11 317.030373
treatedM_c49c187f-98e7-463d-a18b-accf3205eb81.htseq.counts
ENSG00000000003.13 3603.133030
ENSG00000000005.5 3.610172
ENSG00000000419.11 981.307177
ENSG00000000457.12 600.059336
ENSG00000000460.15 338.614376
ENSG00000000938.11 245.204046
treatedM_c4c4578a-f1c2-462a-873f-c8db84fe3a6f.htseq.counts
ENSG00000000003.13 3315.768855
ENSG00000000005.5 3.322247
ENSG00000000419.11 903.044031
ENSG00000000457.12 552.202220
ENSG00000000460.15 311.608534
ENSG00000000938.11 225.648049
treatedM_c7e1465f-cc5a-4dc9-9bb1-6f1112da7aad.htseq.counts
ENSG00000000003.13 5592.198160
ENSG00000000005.5 5.603124
ENSG00000000419.11 1523.025696
ENSG00000000457.12 931.314688
ENSG00000000460.15 525.542264
ENSG00000000938.11 380.565914
treatedM_dad3353e-6721-4e2e-ae06-04eea4c00f12.htseq.counts
ENSG00000000003.13 5816.058357
ENSG00000000005.5 5.827421
ENSG00000000419.11 1583.993641
ENSG00000000457.12 968.595965
ENSG00000000460.15 546.580145
ENSG00000000938.11 395.800274
treatedM_dcf5a350-7cff-475a-8f53-376422f990eb.htseq.counts
ENSG00000000003.13 4039.896492
ENSG00000000005.5 4.047789
ENSG00000000419.11 1100.258966
ENSG00000000457.12 672.797142
ENSG00000000460.15 379.660429
ENSG00000000938.11 274.927114
treatedM_e48c520a-d62c-440d-b17c-5b5ff03d41a2.htseq.counts
ENSG00000000003.13 4338.526111
ENSG00000000005.5 4.347002
ENSG00000000419.11 1181.590237
ENSG00000000457.12 722.530385
ENSG00000000460.15 407.724972
ENSG00000000938.11 295.249758
treatedM_e4d48564-e021-4bbc-bc00-145e9d64b501.htseq.counts
ENSG00000000003.13 5336.018150
ENSG00000000005.5 5.346443
ENSG00000000419.11 1453.255504
ENSG00000000457.12 888.650927
ENSG00000000460.15 501.467040
ENSG00000000938.11 363.132093
untreatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts
ENSG00000000003.13 4504.349177
ENSG00000000005.5 4.337283
ENSG00000000419.11 1135.530590
ENSG00000000457.12 651.582832
ENSG00000000460.15 277.800400
ENSG00000000938.11 248.635727
untreatedF_104c7c7e-583a-4496-a685-5d277e28c11b.htseq.counts
ENSG00000000003.13 5607.215962
ENSG00000000005.5 5.399244
ENSG00000000419.11 1413.559429
ENSG00000000457.12 811.119545
ENSG00000000460.15 345.818403
ENSG00000000938.11 309.512909
untreatedF_10a32136-8a4d-4af3-8e70-543b2a336c91.htseq.counts
ENSG00000000003.13 2929.506533
ENSG00000000005.5 2.820851
ENSG00000000419.11 738.518297
ENSG00000000457.12 423.771801
ENSG00000000460.15 180.673846
ENSG00000000938.11 161.705933
untreatedF_16fc8611-259d-4cd8-9e93-60d97bebb6bf.htseq.counts
ENSG00000000003.13 5900.424472
ENSG00000000005.5 5.681578
ENSG00000000419.11 1487.476264
ENSG00000000457.12 853.534026
ENSG00000000460.15 363.901691
ENSG00000000938.11 325.697736
untreatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts
ENSG00000000003.13 6056.223056
ENSG00000000005.5 5.831598
ENSG00000000419.11 1526.752539
ENSG00000000457.12 876.071284
ENSG00000000460.15 373.510384
ENSG00000000938.11 334.297668
untreatedF_2f3f2e57-4c69-469f-be9c-01cc4f704427.htseq.counts
ENSG00000000003.13 3987.678514
ENSG00000000005.5 3.839775
ENSG00000000419.11 1005.279733
ENSG00000000457.12 576.843126
ENSG00000000460.15 245.935349
ENSG00000000938.11 220.116005
untreatedF_3e0831c0-6726-4f71-9cba-faaeea280cf9.htseq.counts
ENSG00000000003.13 6454.197647
ENSG00000000005.5 6.214811
ENSG00000000419.11 1627.080534
ENSG00000000457.12 933.640847
ENSG00000000460.15 398.054995
ENSG00000000938.11 356.265481
untreatedF_6c86f9ae-9e88-4eae-9ba3-4866943cca7f.htseq.counts
ENSG00000000003.13 5589.864468
ENSG00000000005.5 5.382536
ENSG00000000419.11 1409.185178
ENSG00000000457.12 808.609541
ENSG00000000460.15 344.748270
ENSG00000000938.11 308.555124
untreatedF_73756e81-79a2-4bff-96e7-99f038306b3e.htseq.counts
ENSG00000000003.13 6082.864700
ENSG00000000005.5 5.857251
ENSG00000000419.11 1533.468804
ENSG00000000457.12 879.925168
ENSG00000000460.15 375.153476
ENSG00000000938.11 335.768261
untreatedF_8d78e34d-490b-4f2e-ba1a-427afe96d26e.htseq.counts
ENSG00000000003.13 5324.701596
ENSG00000000005.5 5.127208
ENSG00000000419.11 1342.338551
ENSG00000000457.12 770.252040
ENSG00000000460.15 328.394664
ENSG00000000938.11 293.918389
untreatedF_92ee9ee1-8f03-4b53-b6c2-04d22fd692ae.htseq.counts
ENSG00000000003.13 4749.523958
ENSG00000000005.5 4.573364
ENSG00000000419.11 1197.338291
ENSG00000000457.12 687.048927
ENSG00000000460.15 292.921265
ENSG00000000938.11 262.169139
untreatedF_98380eb7-4de1-4e3a-989c-eedf6eec2977.htseq.counts
ENSG00000000003.13 4612.299530
ENSG00000000005.5 4.441229
ENSG00000000419.11 1162.744495
ENSG00000000457.12 667.198539
ENSG00000000460.15 284.458110
ENSG00000000938.11 254.594483
untreatedF_99e82612-a602-459b-8a60-80a812bf9d88.htseq.counts
ENSG00000000003.13 4374.358038
ENSG00000000005.5 4.212113
ENSG00000000419.11 1102.760281
ENSG00000000457.12 632.778785
ENSG00000000460.15 269.783351
ENSG00000000938.11 241.460342
untreatedF_b39bcb10-164d-48c5-89ba-9ef668765db6.htseq.counts
ENSG00000000003.13 4958.78099
ENSG00000000005.5 4.77486
ENSG00000000419.11 1250.09125
ENSG00000000457.12 717.31929
ENSG00000000460.15 305.82694
ENSG00000000938.11 273.71992
untreatedF_b49faa57-5a15-4193-9552-130d9a63eb40.htseq.counts
ENSG00000000003.13 9375.598888
ENSG00000000005.5 9.027858
ENSG00000000419.11 2363.555515
ENSG00000000457.12 1356.240165
ENSG00000000460.15 578.228956
ENSG00000000938.11 517.524010
untreatedF_d2bf6185-08a5-4a73-9f87-7164cca1ccf6.htseq.counts
ENSG00000000003.13 1650.499750
ENSG00000000005.5 1.589283
ENSG00000000419.11 416.085184
ENSG00000000457.12 238.755314
ENSG00000000460.15 101.792617
ENSG00000000938.11 91.105993
untreatedF_edf02d6d-619f-4325-a585-dca7bf5afbeb.htseq.counts
ENSG00000000003.13 2872.655275
ENSG00000000005.5 2.766108
ENSG00000000419.11 724.186295
ENSG00000000457.12 415.547904
ENSG00000000460.15 177.167612
ENSG00000000938.11 158.567799
untreatedF_eed21a5a-30db-45a0-9b60-3f6a91b38cb6.htseq.counts
ENSG00000000003.13 7821.524199
ENSG00000000005.5 7.531424
ENSG00000000419.11 1971.778750
ENSG00000000457.12 1131.433352
ENSG00000000460.15 482.383241
ENSG00000000938.11 431.740587
untreatedF_f7a956f8-4dc2-437a-92db-8baf8f9b3f14.htseq.counts
ENSG00000000003.13 6584.478980
ENSG00000000005.5 6.340261
ENSG00000000419.11 1659.924000
ENSG00000000457.12 952.486872
ENSG00000000460.15 406.089942
ENSG00000000938.11 363.456885
untreatedF_ffeed225-c2a3-4b4c-954c-4816903782a9.htseq.counts
ENSG00000000003.13 7106.384594
ENSG00000000005.5 6.842809
ENSG00000000419.11 1791.494570
ENSG00000000457.12 1027.983847
ENSG00000000460.15 438.277853
ENSG00000000938.11 392.265571
untreatedM_09f395dd-d007-41e0-8a51-a6533ae51087.htseq.counts
ENSG00000000003.13 4440.766989
ENSG00000000005.5 4.276059
ENSG00000000419.11 1119.501744
ENSG00000000457.12 642.385263
ENSG00000000460.15 273.879044
ENSG00000000938.11 245.126052
untreatedM_10d6deec-fd89-4115-92f5-c9def40428b5.htseq.counts
ENSG00000000003.13 4981.548721
ENSG00000000005.5 4.796783
ENSG00000000419.11 1255.830917
ENSG00000000457.12 720.612789
ENSG00000000460.15 307.231117
ENSG00000000938.11 274.976681
untreatedM_1d782a72-61f3-4002-bee5-67daeb8244e3.htseq.counts
ENSG00000000003.13 3461.264997
ENSG00000000005.5 3.332887
ENSG00000000419.11 872.572736
ENSG00000000457.12 500.694053
ENSG00000000460.15 213.469419
ENSG00000000938.11 191.058487
untreatedM_2cc2e3ce-68cd-4690-9fff-5ecf86c2f57a.htseq.counts
ENSG00000000003.13 3866.885923
ENSG00000000005.5 3.723463
ENSG00000000419.11 974.828345
ENSG00000000457.12 559.369707
ENSG00000000460.15 238.485609
ENSG00000000938.11 213.448371
untreatedM_35a2858e-9787-457c-9435-33bd62332aa3.htseq.counts
ENSG00000000003.13 5551.613004
ENSG00000000005.5 5.345704
ENSG00000000419.11 1399.542119
ENSG00000000457.12 803.076222
ENSG00000000460.15 342.389156
ENSG00000000938.11 306.443680
untreatedM_78e27399-eb4a-4b86-992e-3e052bad044c.htseq.counts
ENSG00000000003.13 5620.875187
ENSG00000000005.5 5.412397
ENSG00000000419.11 1417.002872
ENSG00000000457.12 813.095439
ENSG00000000460.15 346.660820
ENSG00000000938.11 310.266885
untreatedM_89eb1fc8-6b38-446e-a4b0-fb9c520298dc.htseq.counts
ENSG00000000003.13 4739.302223
ENSG00000000005.5 4.563521
ENSG00000000419.11 1194.761427
ENSG00000000457.12 685.570288
ENSG00000000460.15 292.290851
ENSG00000000938.11 261.604909
untreatedM_9700948e-21f5-42ae-9cb5-f18c745887ce.htseq.counts
ENSG00000000003.13 3659.400181
ENSG00000000005.5 3.523673
ENSG00000000419.11 922.521919
ENSG00000000457.12 529.355571
ENSG00000000460.15 225.689172
ENSG00000000938.11 201.995358
untreatedM_a3a6543b-ea79-4724-a140-c83392b1842b.htseq.counts
ENSG00000000003.13 3442.373899
ENSG00000000005.5 3.314696
ENSG00000000419.11 867.810357
ENSG00000000457.12 497.961336
ENSG00000000460.15 212.304333
ENSG00000000938.11 190.015717
untreatedM_ad69efb1-61ab-4492-8468-9006dd00e915.htseq.counts
ENSG00000000003.13 5915.867879
ENSG00000000005.5 5.696448
ENSG00000000419.11 1491.369492
ENSG00000000457.12 855.768013
ENSG00000000460.15 364.854144
ENSG00000000938.11 326.550197
untreatedM_b04fb368-4b93-4ef9-ae9f-020484c1686b.htseq.counts
ENSG00000000003.13 5619.750386
ENSG00000000005.5 5.411314
ENSG00000000419.11 1416.719313
ENSG00000000457.12 812.932729
ENSG00000000460.15 346.591449
ENSG00000000938.11 310.204797
untreatedM_b1820867-e6b2-4228-ad00-19d1d7fddf67.htseq.counts
ENSG00000000003.13 4647.045287
ENSG00000000005.5 4.474686
ENSG00000000419.11 1171.503778
ENSG00000000457.12 672.224734
ENSG00000000460.15 286.601014
ENSG00000000938.11 256.512415
untreatedM_bdd35388-0850-40ac-a21a-6bfee0c34eef.htseq.counts
ENSG00000000003.13 5350.516342
ENSG00000000005.5 5.152066
ENSG00000000419.11 1348.846357
ENSG00000000457.12 773.986308
ENSG00000000460.15 329.986758
ENSG00000000938.11 295.343338
untreatedM_cfb40dba-37a7-4ae7-a2c9-34becc30f939.htseq.counts
ENSG00000000003.13 4096.716359
ENSG00000000005.5 3.944769
ENSG00000000419.11 1032.767790
ENSG00000000457.12 592.616145
ENSG00000000460.15 252.660129
ENSG00000000938.11 226.134789
untreatedM_d1cbb686-93d8-4349-8bf5-756887c60ebc.htseq.counts
ENSG00000000003.13 4659.690122
ENSG00000000005.5 4.486862
ENSG00000000419.11 1174.691496
ENSG00000000457.12 674.053890
ENSG00000000460.15 287.380869
ENSG00000000938.11 257.210397
untreatedM_d7a9f4a9-6998-45dc-9bc4-06f3f7110429.htseq.counts
ENSG00000000003.13 4740.739575
ENSG00000000005.5 4.564906
ENSG00000000419.11 1195.123778
ENSG00000000457.12 685.778210
ENSG00000000460.15 292.379498
ENSG00000000938.11 261.684249
untreatedM_e035a46e-6114-4a64-b5ae-9e6209223493.htseq.counts
ENSG00000000003.13 5511.063043
ENSG00000000005.5 5.306658
ENSG00000000419.11 1389.319616
ENSG00000000457.12 797.210412
ENSG00000000460.15 339.888286
ENSG00000000938.11 304.205361
untreatedM_e208fa4d-3f65-4615-bd6e-870a52bb6da3.htseq.counts
ENSG00000000003.13 6466.898511
ENSG00000000005.5 6.227041
ENSG00000000419.11 1630.282377
ENSG00000000457.12 935.478108
ENSG00000000460.15 398.838306
ENSG00000000938.11 356.966556
untreatedM_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts
ENSG00000000003.13 1936.565431
ENSG00000000005.5 1.864738
ENSG00000000419.11 488.201336
ENSG00000000457.12 280.136539
ENSG00000000460.15 119.435379
ENSG00000000938.11 106.896543
untreatedM_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts
ENSG00000000003.13 3112.455176
ENSG00000000005.5 2.997014
ENSG00000000419.11 784.639007
ENSG00000000457.12 450.236489
ENSG00000000460.15 191.956986
ENSG00000000938.11 171.804522head(assays(dds)[["cooks"]]) treatedF_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts
ENSG00000000003.13 5.097136e-06
ENSG00000000005.5 4.540728e-03
ENSG00000000419.11 2.339074e-02
ENSG00000000457.12 3.270106e-02
ENSG00000000460.15 2.824245e-02
ENSG00000000938.11 2.073188e-02
treatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts
ENSG00000000003.13 1.511184e-05
ENSG00000000005.5 5.429639e-03
ENSG00000000419.11 1.060596e-03
ENSG00000000457.12 1.681533e-02
ENSG00000000460.15 2.704269e-02
ENSG00000000938.11 1.645325e-03
treatedF_160aee04-df36-4e94-90c5-b01b2991ba48.htseq.counts
ENSG00000000003.13 0.0003694600
ENSG00000000005.5 0.0065179702
ENSG00000000419.11 0.0006164506
ENSG00000000457.12 0.0003061180
ENSG00000000460.15 0.0314551615
ENSG00000000938.11 0.0024000702
treatedF_1a228668-b2a0-469f-8801-fdc8be449b44.htseq.counts
ENSG00000000003.13 0.0003266357
ENSG00000000005.5 0.0238143309
ENSG00000000419.11 0.0052478592
ENSG00000000457.12 0.0243284585
ENSG00000000460.15 0.0005685959
ENSG00000000938.11 0.0063354828
treatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts
ENSG00000000003.13 0.008548513
ENSG00000000005.5 0.025982939
ENSG00000000419.11 0.003617809
ENSG00000000457.12 0.002604817
ENSG00000000460.15 0.001545922
ENSG00000000938.11 0.010297829
treatedF_28ef84e7-f566-40a7-b239-26d3e23c878a.htseq.counts
ENSG00000000003.13 0.038444149
ENSG00000000005.5 0.013991799
ENSG00000000419.11 0.008067015
ENSG00000000457.12 0.015691953
ENSG00000000460.15 0.028631791
ENSG00000000938.11 0.114351840
treatedF_2baabd07-b92c-4ac1-8de7-3d8df4f4a80b.htseq.counts
ENSG00000000003.13 0.014681702
ENSG00000000005.5 0.009759370
ENSG00000000419.11 0.001360615
ENSG00000000457.12 0.030812701
ENSG00000000460.15 0.196656249
ENSG00000000938.11 0.003538153
treatedF_3e0831c0-6726-4f71-9cba-faaeea280cf9.htseq.counts
ENSG00000000003.13 0.0076365755
ENSG00000000005.5 0.0111009122
ENSG00000000419.11 0.0284720514
ENSG00000000457.12 0.0050802522
ENSG00000000460.15 0.0001723787
ENSG00000000938.11 0.2462321206
treatedF_554f6de3-63c7-47b1-a75a-dcfc73f54e96.htseq.counts
ENSG00000000003.13 0.002525971
ENSG00000000005.5 0.024254704
ENSG00000000419.11 0.368995380
ENSG00000000457.12 0.020506818
ENSG00000000460.15 0.009793162
ENSG00000000938.11 0.098957636
treatedF_6cd9b90b-0c6c-44bd-8536-5a93553a7730.htseq.counts
ENSG00000000003.13 0.0106473831
ENSG00000000005.5 0.0291974706
ENSG00000000419.11 0.0132479102
ENSG00000000457.12 0.0003412658
ENSG00000000460.15 0.0263022772
ENSG00000000938.11 0.0117082156
treatedF_7a3a131b-883d-4f82-8b3b-ede7733f68d8.htseq.counts
ENSG00000000003.13 3.948808e-06
ENSG00000000005.5 7.767884e-03
ENSG00000000419.11 7.557361e-03
ENSG00000000457.12 5.833968e-04
ENSG00000000460.15 2.016568e-02
ENSG00000000938.11 2.096087e-02
treatedF_7b77e8cb-3f1d-4af7-b609-d75f912484c4.htseq.counts
ENSG00000000003.13 0.0295731004
ENSG00000000005.5 0.0284520336
ENSG00000000419.11 0.0106571545
ENSG00000000457.12 0.0319810916
ENSG00000000460.15 0.0001320881
ENSG00000000938.11 0.0020804718
treatedF_89e20bd9-15e5-48e6-9dfe-0b919c817216.htseq.counts
ENSG00000000003.13 2.199209e-02
ENSG00000000005.5 2.460360e-02
ENSG00000000419.11 3.485329e-02
ENSG00000000457.12 2.790229e-05
ENSG00000000460.15 1.850636e-02
ENSG00000000938.11 2.890712e-03
treatedF_98380eb7-4de1-4e3a-989c-eedf6eec2977.htseq.counts
ENSG00000000003.13 1.198017e-04
ENSG00000000005.5 2.977119e+00
ENSG00000000419.11 2.174147e-06
ENSG00000000457.12 9.775186e-05
ENSG00000000460.15 2.672377e-02
ENSG00000000938.11 1.252959e-02
treatedF_9ccd91ba-7180-443a-b03b-f9b398c679e4.htseq.counts
ENSG00000000003.13 0.0002858424
ENSG00000000005.5 0.0121940009
ENSG00000000419.11 0.0026306094
ENSG00000000457.12 0.0229097153
ENSG00000000460.15 0.0131676638
ENSG00000000938.11 0.0102711102
treatedF_aaec3912-230f-4418-986d-878e77480f94.htseq.counts
ENSG00000000003.13 0.011983428
ENSG00000000005.5 0.013569947
ENSG00000000419.11 0.053027247
ENSG00000000457.12 0.003353263
ENSG00000000460.15 0.002565868
ENSG00000000938.11 0.009330105
treatedF_d5fd715a-cf6d-4b29-ad9d-b3e58c55262a.htseq.counts
ENSG00000000003.13 0.0024740134
ENSG00000000005.5 0.0246096284
ENSG00000000419.11 0.0050487511
ENSG00000000457.12 0.0009189565
ENSG00000000460.15 0.0002765465
ENSG00000000938.11 0.0036816752
treatedF_ee857c4c-e6a9-4f39-b95b-20fe46dd1ab7.htseq.counts
ENSG00000000003.13 6.062776e-05
ENSG00000000005.5 6.469538e-02
ENSG00000000419.11 1.155467e-03
ENSG00000000457.12 6.198118e-05
ENSG00000000460.15 8.161691e-05
ENSG00000000938.11 1.016986e-02
treatedF_eea3fcee-ee51-461b-abb4-c393c084e018.htseq.counts
ENSG00000000003.13 0.002096768
ENSG00000000005.5 0.003051693
ENSG00000000419.11 0.036213637
ENSG00000000457.12 0.005640711
ENSG00000000460.15 0.014680014
ENSG00000000938.11 0.006031034
treatedF_f6ae6ac1-3e00-4021-a6e7-fbb0d5f12836.htseq.counts
ENSG00000000003.13 0.0156436424
ENSG00000000005.5 0.0189827972
ENSG00000000419.11 0.0305338996
ENSG00000000457.12 0.0004083158
ENSG00000000460.15 0.0045101211
ENSG00000000938.11 0.0153672900
treatedM_04e7f1a4-3173-4a6f-af60-04e1f2e29868.htseq.counts
ENSG00000000003.13 6.870673e-03
ENSG00000000005.5 1.673185e-02
ENSG00000000419.11 1.278957e-03
ENSG00000000457.12 5.270714e-06
ENSG00000000460.15 2.382055e-04
ENSG00000000938.11 3.091657e-04
treatedM_05ac7b05-e459-4833-97fc-530185a7a55f.htseq.counts
ENSG00000000003.13 0.192866610
ENSG00000000005.5 0.005481857
ENSG00000000419.11 0.015823998
ENSG00000000457.12 0.032971961
ENSG00000000460.15 0.017033661
ENSG00000000938.11 0.002250537
treatedM_063411ca-874c-49ff-85c2-b6946f43df4e.htseq.counts
ENSG00000000003.13 0.100581268
ENSG00000000005.5 0.006185201
ENSG00000000419.11 0.144308511
ENSG00000000457.12 0.003757016
ENSG00000000460.15 0.029966974
ENSG00000000938.11 0.002859054
treatedM_1c6bb58a-3869-41f5-9fef-9070bbfedbad.htseq.counts
ENSG00000000003.13 0.0190103793
ENSG00000000005.5 0.0001003638
ENSG00000000419.11 0.0016601359
ENSG00000000457.12 0.0005791009
ENSG00000000460.15 0.0058002730
ENSG00000000938.11 0.0353449192
treatedM_51c2d807-7ee6-4b42-a6a7-3eb14f987bc0.htseq.counts
ENSG00000000003.13 0.0034551553
ENSG00000000005.5 0.0211223018
ENSG00000000419.11 0.0154144936
ENSG00000000457.12 0.0025017237
ENSG00000000460.15 0.0000161509
ENSG00000000938.11 0.0041627244
treatedM_57ef7745-338c-4fb2-ac9b-9f57daba2c51.htseq.counts
ENSG00000000003.13 3.838229e-02
ENSG00000000005.5 3.544631e-02
ENSG00000000419.11 1.693698e-06
ENSG00000000457.12 1.048623e-02
ENSG00000000460.15 6.843557e-02
ENSG00000000938.11 1.152993e-02
treatedM_77b9a94e-aaa7-409c-9849-86ba7d5c061a.htseq.counts
ENSG00000000003.13 0.008492739
ENSG00000000005.5 0.024816856
ENSG00000000419.11 0.014502805
ENSG00000000457.12 0.010295546
ENSG00000000460.15 0.016814097
ENSG00000000938.11 0.003697086
treatedM_79f4b12a-ed1c-46d5-ae3d-a0422454eaf4.htseq.counts
ENSG00000000003.13 1.026927e-02
ENSG00000000005.5 1.591205e-02
ENSG00000000419.11 4.687189e-03
ENSG00000000457.12 4.541374e-07
ENSG00000000460.15 2.518872e-03
ENSG00000000938.11 5.980860e-03
treatedM_7f86ace4-9a17-4b17-8a79-3b2ef53f62c3.htseq.counts
ENSG00000000003.13 0.009588653
ENSG00000000005.5 0.003871843
ENSG00000000419.11 0.013613150
ENSG00000000457.12 0.001735624
ENSG00000000460.15 0.037790013
ENSG00000000938.11 0.020908866
treatedM_9700948e-21f5-42ae-9cb5-f18c745887ce.htseq.counts
ENSG00000000003.13 0.006972302
ENSG00000000005.5 0.020022171
ENSG00000000419.11 0.009419123
ENSG00000000457.12 0.017296544
ENSG00000000460.15 0.015040745
ENSG00000000938.11 0.011337716
treatedM_a3a6543b-ea79-4724-a140-c83392b1842b.htseq.counts
ENSG00000000003.13 5.575653e-04
ENSG00000000005.5 8.580543e-03
ENSG00000000419.11 1.631930e-03
ENSG00000000457.12 1.723261e-02
ENSG00000000460.15 2.184944e-02
ENSG00000000938.11 6.469058e-06
treatedM_b5d92dd1-d98b-4b9e-953e-81c04dfb1e0d.htseq.counts
ENSG00000000003.13 0.0031957549
ENSG00000000005.5 0.0289717456
ENSG00000000419.11 0.0001130022
ENSG00000000457.12 0.0184028492
ENSG00000000460.15 0.5309545497
ENSG00000000938.11 0.0152771353
treatedM_bdd35388-0850-40ac-a21a-6bfee0c34eef.htseq.counts
ENSG00000000003.13 2.529449e-03
ENSG00000000005.5 1.244655e-04
ENSG00000000419.11 2.418367e-03
ENSG00000000457.12 6.624570e-07
ENSG00000000460.15 1.193138e-02
ENSG00000000938.11 7.146091e-03
treatedM_c49c187f-98e7-463d-a18b-accf3205eb81.htseq.counts
ENSG00000000003.13 0.006119266
ENSG00000000005.5 0.116131537
ENSG00000000419.11 0.000943359
ENSG00000000457.12 0.003409753
ENSG00000000460.15 0.021343084
ENSG00000000938.11 0.006487061
treatedM_c4c4578a-f1c2-462a-873f-c8db84fe3a6f.htseq.counts
ENSG00000000003.13 0.005095565
ENSG00000000005.5 0.003247638
ENSG00000000419.11 0.003039194
ENSG00000000457.12 0.013984406
ENSG00000000460.15 0.018450754
ENSG00000000938.11 0.005272462
treatedM_c7e1465f-cc5a-4dc9-9bb1-6f1112da7aad.htseq.counts
ENSG00000000003.13 0.006509720
ENSG00000000005.5 0.046437310
ENSG00000000419.11 0.014094488
ENSG00000000457.12 0.008642259
ENSG00000000460.15 0.013154302
ENSG00000000938.11 0.013754629
treatedM_dad3353e-6721-4e2e-ae06-04eea4c00f12.htseq.counts
ENSG00000000003.13 4.626449e-03
ENSG00000000005.5 1.168301e-02
ENSG00000000419.11 1.385398e-05
ENSG00000000457.12 3.303839e-02
ENSG00000000460.15 1.011274e-02
ENSG00000000938.11 1.514369e-02
treatedM_dcf5a350-7cff-475a-8f53-376422f990eb.htseq.counts
ENSG00000000003.13 0.0001031364
ENSG00000000005.5 0.0228732608
ENSG00000000419.11 0.0274181533
ENSG00000000457.12 0.0001068395
ENSG00000000460.15 0.0033405871
ENSG00000000938.11 0.0195321157
treatedM_e48c520a-d62c-440d-b17c-5b5ff03d41a2.htseq.counts
ENSG00000000003.13 0.4258410615
ENSG00000000005.5 0.0140616183
ENSG00000000419.11 0.0005312089
ENSG00000000457.12 0.0976748060
ENSG00000000460.15 2.0451344816
ENSG00000000938.11 0.0170024847
treatedM_e4d48564-e021-4bbc-bc00-145e9d64b501.htseq.counts
ENSG00000000003.13 0.0084229260
ENSG00000000005.5 0.0172612935
ENSG00000000419.11 0.0003677522
ENSG00000000457.12 0.0014009858
ENSG00000000460.15 0.0098584068
ENSG00000000938.11 0.0083591585
untreatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts
ENSG00000000003.13 0.001208439
ENSG00000000005.5 0.006843035
ENSG00000000419.11 0.003566997
ENSG00000000457.12 0.016689502
ENSG00000000460.15 0.021110996
ENSG00000000938.11 0.003150451
untreatedF_104c7c7e-583a-4496-a685-5d277e28c11b.htseq.counts
ENSG00000000003.13 4.454603e-05
ENSG00000000005.5 2.608687e-02
ENSG00000000419.11 2.288353e-01
ENSG00000000457.12 8.294396e-03
ENSG00000000460.15 2.909856e-02
ENSG00000000938.11 4.624667e-03
untreatedF_10a32136-8a4d-4af3-8e70-543b2a336c91.htseq.counts
ENSG00000000003.13 1.730598e-03
ENSG00000000005.5 1.843589e-02
ENSG00000000419.11 3.114133e-05
ENSG00000000457.12 2.647160e-03
ENSG00000000460.15 4.740210e-03
ENSG00000000938.11 1.165325e-01
untreatedF_16fc8611-259d-4cd8-9e93-60d97bebb6bf.htseq.counts
ENSG00000000003.13 0.0001758587
ENSG00000000005.5 0.0266560695
ENSG00000000419.11 0.0040837670
ENSG00000000457.12 0.0017847568
ENSG00000000460.15 0.0050409136
ENSG00000000938.11 0.0009593574
untreatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts
ENSG00000000003.13 0.0125194575
ENSG00000000005.5 0.0269440370
ENSG00000000419.11 0.0007525892
ENSG00000000457.12 0.0027032236
ENSG00000000460.15 0.0155852901
ENSG00000000938.11 0.0092749039
untreatedF_2f3f2e57-4c69-469f-be9c-01cc4f704427.htseq.counts
ENSG00000000003.13 7.904144e-03
ENSG00000000005.5 3.851976e-05
ENSG00000000419.11 4.516748e-04
ENSG00000000457.12 1.436005e-01
ENSG00000000460.15 1.188869e-02
ENSG00000000938.11 7.486486e-05
untreatedF_3e0831c0-6726-4f71-9cba-faaeea280cf9.htseq.counts
ENSG00000000003.13 0.011551089
ENSG00000000005.5 0.012711766
ENSG00000000419.11 0.016796777
ENSG00000000457.12 0.005224965
ENSG00000000460.15 0.003064031
ENSG00000000938.11 0.297340049
untreatedF_6c86f9ae-9e88-4eae-9ba3-4866943cca7f.htseq.counts
ENSG00000000003.13 0.001188584
ENSG00000000005.5 0.026052011
ENSG00000000419.11 0.001076475
ENSG00000000457.12 0.008681698
ENSG00000000460.15 0.008663577
ENSG00000000938.11 0.007793832
untreatedF_73756e81-79a2-4bff-96e7-99f038306b3e.htseq.counts
ENSG00000000003.13 0.0031696581
ENSG00000000005.5 0.0064231702
ENSG00000000419.11 0.0006483591
ENSG00000000457.12 0.0035071075
ENSG00000000460.15 0.0001575320
ENSG00000000938.11 0.0013674522
untreatedF_8d78e34d-490b-4f2e-ba1a-427afe96d26e.htseq.counts
ENSG00000000003.13 0.002407762
ENSG00000000005.5 0.016524335
ENSG00000000419.11 0.041198086
ENSG00000000457.12 0.004112632
ENSG00000000460.15 0.005587493
ENSG00000000938.11 0.015502439
untreatedF_92ee9ee1-8f03-4b53-b6c2-04d22fd692ae.htseq.counts
ENSG00000000003.13 3.987154e-02
ENSG00000000005.5 2.084514e-01
ENSG00000000419.11 1.309392e-02
ENSG00000000457.12 6.392765e-05
ENSG00000000460.15 1.610602e-03
ENSG00000000938.11 1.682362e-02
untreatedF_98380eb7-4de1-4e3a-989c-eedf6eec2977.htseq.counts
ENSG00000000003.13 0.0004773145
ENSG00000000005.5 2.5089255138
ENSG00000000419.11 0.0007667327
ENSG00000000457.12 0.0001152018
ENSG00000000460.15 0.0207369696
ENSG00000000938.11 0.0115648919
untreatedF_99e82612-a602-459b-8a60-80a812bf9d88.htseq.counts
ENSG00000000003.13 1.237179e-02
ENSG00000000005.5 3.262195e-01
ENSG00000000419.11 1.511198e-05
ENSG00000000457.12 6.071959e-02
ENSG00000000460.15 1.479259e-04
ENSG00000000938.11 1.887587e-02
untreatedF_b39bcb10-164d-48c5-89ba-9ef668765db6.htseq.counts
ENSG00000000003.13 0.0214103299
ENSG00000000005.5 0.0112618735
ENSG00000000419.11 0.0005927917
ENSG00000000457.12 0.0001405413
ENSG00000000460.15 0.5010664551
ENSG00000000938.11 0.0049578850
untreatedF_b49faa57-5a15-4193-9552-130d9a63eb40.htseq.counts
ENSG00000000003.13 4.957875e-03
ENSG00000000005.5 3.139755e-02
ENSG00000000419.11 8.837287e-03
ENSG00000000457.12 4.572460e-03
ENSG00000000460.15 1.558606e-05
ENSG00000000938.11 2.926986e-03
untreatedF_d2bf6185-08a5-4a73-9f87-7164cca1ccf6.htseq.counts
ENSG00000000003.13 0.060345066
ENSG00000000005.5 0.001646551
ENSG00000000419.11 0.007439980
ENSG00000000457.12 0.009416688
ENSG00000000460.15 0.015894461
ENSG00000000938.11 0.012028953
untreatedF_edf02d6d-619f-4325-a585-dca7bf5afbeb.htseq.counts
ENSG00000000003.13 0.000687812
ENSG00000000005.5 0.018202933
ENSG00000000419.11 0.001088213
ENSG00000000457.12 0.315963472
ENSG00000000460.15 0.024198774
ENSG00000000938.11 0.016018744
untreatedF_eed21a5a-30db-45a0-9b60-3f6a91b38cb6.htseq.counts
ENSG00000000003.13 0.054843403
ENSG00000000005.5 0.029643682
ENSG00000000419.11 0.005202340
ENSG00000000457.12 0.009660807
ENSG00000000460.15 0.015159195
ENSG00000000938.11 0.001581403
untreatedF_f7a956f8-4dc2-437a-92db-8baf8f9b3f14.htseq.counts
ENSG00000000003.13 0.0057179754
ENSG00000000005.5 0.0278529562
ENSG00000000419.11 0.0271216272
ENSG00000000457.12 0.0018794787
ENSG00000000460.15 0.0076129307
ENSG00000000938.11 0.0005948193
untreatedF_ffeed225-c2a3-4b4c-954c-4816903782a9.htseq.counts
ENSG00000000003.13 5.469442e-03
ENSG00000000005.5 1.435516e-02
ENSG00000000419.11 4.186912e-04
ENSG00000000457.12 7.588330e-03
ENSG00000000460.15 3.551899e-05
ENSG00000000938.11 4.920432e-02
untreatedM_09f395dd-d007-41e0-8a51-a6533ae51087.htseq.counts
ENSG00000000003.13 4.693645e-02
ENSG00000000005.5 2.339752e-02
ENSG00000000419.11 4.018063e-05
ENSG00000000457.12 4.733184e-04
ENSG00000000460.15 4.404427e-03
ENSG00000000938.11 4.442557e-04
untreatedM_10d6deec-fd89-4115-92f5-c9def40428b5.htseq.counts
ENSG00000000003.13 1.441060e-02
ENSG00000000005.5 2.473801e-02
ENSG00000000419.11 1.281046e-05
ENSG00000000457.12 1.120623e-02
ENSG00000000460.15 4.122888e-03
ENSG00000000938.11 6.891180e-03
untreatedM_1d782a72-61f3-4002-bee5-67daeb8244e3.htseq.counts
ENSG00000000003.13 0.160847404
ENSG00000000005.5 0.451444319
ENSG00000000419.11 0.061652187
ENSG00000000457.12 0.028773478
ENSG00000000460.15 0.007672218
ENSG00000000938.11 0.001042582
untreatedM_2cc2e3ce-68cd-4690-9fff-5ecf86c2f57a.htseq.counts
ENSG00000000003.13 2.620074e-03
ENSG00000000005.5 8.308688e-02
ENSG00000000419.11 2.111761e-05
ENSG00000000457.12 1.605681e-02
ENSG00000000460.15 2.452265e-02
ENSG00000000938.11 1.939534e-02
untreatedM_35a2858e-9787-457c-9435-33bd62332aa3.htseq.counts
ENSG00000000003.13 0.0220960704
ENSG00000000005.5 0.0016460363
ENSG00000000419.11 0.0021284573
ENSG00000000457.12 0.0115394921
ENSG00000000460.15 0.1135153627
ENSG00000000938.11 0.0007718129
untreatedM_78e27399-eb4a-4b86-992e-3e052bad044c.htseq.counts
ENSG00000000003.13 0.0104068685
ENSG00000000005.5 0.0103804639
ENSG00000000419.11 0.0245228041
ENSG00000000457.12 0.0138241544
ENSG00000000460.15 0.0107621193
ENSG00000000938.11 0.0004033093
untreatedM_89eb1fc8-6b38-446e-a4b0-fb9c520298dc.htseq.counts
ENSG00000000003.13 0.0066492710
ENSG00000000005.5 0.0147315880
ENSG00000000419.11 0.0205066352
ENSG00000000457.12 0.0119442132
ENSG00000000460.15 0.0435840665
ENSG00000000938.11 0.0002820657
untreatedM_9700948e-21f5-42ae-9cb5-f18c745887ce.htseq.counts
ENSG00000000003.13 0.001708916
ENSG00000000005.5 0.021096633
ENSG00000000419.11 0.014484299
ENSG00000000457.12 0.017602073
ENSG00000000460.15 0.008098658
ENSG00000000938.11 0.010341625
untreatedM_a3a6543b-ea79-4724-a140-c83392b1842b.htseq.counts
ENSG00000000003.13 1.168041e-04
ENSG00000000005.5 9.930513e-03
ENSG00000000419.11 8.269681e-05
ENSG00000000457.12 1.710681e-02
ENSG00000000460.15 1.521302e-02
ENSG00000000938.11 2.338250e-04
untreatedM_ad69efb1-61ab-4492-8468-9006dd00e915.htseq.counts
ENSG00000000003.13 0.014465312
ENSG00000000005.5 0.026685048
ENSG00000000419.11 0.002618653
ENSG00000000457.12 0.013755412
ENSG00000000460.15 0.012753584
ENSG00000000938.11 0.012385014
untreatedM_b04fb368-4b93-4ef9-ae9f-020484c1686b.htseq.counts
ENSG00000000003.13 0.0017463807
ENSG00000000005.5 0.0261119653
ENSG00000000419.11 0.0261559899
ENSG00000000457.12 0.0276678233
ENSG00000000460.15 0.0017723731
ENSG00000000938.11 0.0009690299
untreatedM_b1820867-e6b2-4228-ad00-19d1d7fddf67.htseq.counts
ENSG00000000003.13 0.015978708
ENSG00000000005.5 0.000269299
ENSG00000000419.11 0.091488779
ENSG00000000457.12 0.007561010
ENSG00000000460.15 0.002490302
ENSG00000000938.11 0.004519391
untreatedM_bdd35388-0850-40ac-a21a-6bfee0c34eef.htseq.counts
ENSG00000000003.13 5.651200e-03
ENSG00000000005.5 2.226429e-05
ENSG00000000419.11 2.941987e-04
ENSG00000000457.12 3.721334e-11
ENSG00000000460.15 5.197749e-03
ENSG00000000938.11 6.103367e-03
untreatedM_cfb40dba-37a7-4ae7-a2c9-34becc30f939.htseq.counts
ENSG00000000003.13 3.711464e-03
ENSG00000000005.5 2.244358e-02
ENSG00000000419.11 3.545301e-02
ENSG00000000457.12 2.582771e-03
ENSG00000000460.15 1.216886e-02
ENSG00000000938.11 6.780245e-05
untreatedM_d1cbb686-93d8-4349-8bf5-756887c60ebc.htseq.counts
ENSG00000000003.13 0.018475412
ENSG00000000005.5 0.023961971
ENSG00000000419.11 0.017174311
ENSG00000000457.12 0.005037229
ENSG00000000460.15 0.001411792
ENSG00000000938.11 0.006864603
untreatedM_d7a9f4a9-6998-45dc-9bc4-06f3f7110429.htseq.counts
ENSG00000000003.13 0.0001141442
ENSG00000000005.5 0.0076284038
ENSG00000000419.11 0.0177618249
ENSG00000000457.12 0.0122297802
ENSG00000000460.15 0.0023923007
ENSG00000000938.11 0.0096337355
untreatedM_e035a46e-6114-4a64-b5ae-9e6209223493.htseq.counts
ENSG00000000003.13 0.002848487
ENSG00000000005.5 0.010053139
ENSG00000000419.11 0.002319246
ENSG00000000457.12 0.003839676
ENSG00000000460.15 0.009239785
ENSG00000000938.11 0.034473643
untreatedM_e208fa4d-3f65-4615-bd6e-870a52bb6da3.htseq.counts
ENSG00000000003.13 0.001014947
ENSG00000000005.5 0.019488890
ENSG00000000419.11 0.017584555
ENSG00000000457.12 0.005813947
ENSG00000000460.15 0.006449986
ENSG00000000938.11 0.015804211
untreatedM_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts
ENSG00000000003.13 1.315399e-02
ENSG00000000005.5 7.195567e-05
ENSG00000000419.11 2.308053e-03
ENSG00000000457.12 7.209963e-02
ENSG00000000460.15 3.742431e-02
ENSG00000000938.11 8.734262e-03
untreatedM_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts
ENSG00000000003.13 5.789846e-03
ENSG00000000005.5 1.901526e-08
ENSG00000000419.11 1.787723e-02
ENSG00000000457.12 1.235890e-02
ENSG00000000460.15 1.246501e-02
ENSG00000000938.11 3.538031e-03Dispersion
head(dispersions(dds))[1] 0.32867927 2.60374065 0.07648619 0.14699702 0.59314178 0.53329305head(mcols(dds)$dispersion)[1] 0.32867927 2.60374065 0.07648619 0.14699702 0.59314178 0.53329305Size factors
sizeFactors(dds) treatedF_004c60cf-c08e-49df-b4ce-baca41e11250.htseq.counts
0.5246864
treatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts
0.9459732
treatedF_160aee04-df36-4e94-90c5-b01b2991ba48.htseq.counts
0.7661427
treatedF_1a228668-b2a0-469f-8801-fdc8be449b44.htseq.counts
1.9999975
treatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts
1.2718873
treatedF_28ef84e7-f566-40a7-b239-26d3e23c878a.htseq.counts
1.6055126
treatedF_2baabd07-b92c-4ac1-8de7-3d8df4f4a80b.htseq.counts
1.7113240
treatedF_3e0831c0-6726-4f71-9cba-faaeea280cf9.htseq.counts
1.3554672
treatedF_554f6de3-63c7-47b1-a75a-dcfc73f54e96.htseq.counts
1.0951914
treatedF_6cd9b90b-0c6c-44bd-8536-5a93553a7730.htseq.counts
0.7623290
treatedF_7a3a131b-883d-4f82-8b3b-ede7733f68d8.htseq.counts
1.1058219
treatedF_7b77e8cb-3f1d-4af7-b609-d75f912484c4.htseq.counts
1.5914416
treatedF_89e20bd9-15e5-48e6-9dfe-0b919c817216.htseq.counts
1.1283617
treatedF_98380eb7-4de1-4e3a-989c-eedf6eec2977.htseq.counts
0.9686442
treatedF_9ccd91ba-7180-443a-b03b-f9b398c679e4.htseq.counts
0.5073303
treatedF_aaec3912-230f-4418-986d-878e77480f94.htseq.counts
0.9490629
treatedF_d5fd715a-cf6d-4b29-ad9d-b3e58c55262a.htseq.counts
1.1289456
treatedF_ee857c4c-e6a9-4f39-b95b-20fe46dd1ab7.htseq.counts
0.7758984
treatedF_eea3fcee-ee51-461b-abb4-c393c084e018.htseq.counts
0.8747412
treatedF_f6ae6ac1-3e00-4021-a6e7-fbb0d5f12836.htseq.counts
1.4398577
treatedM_04e7f1a4-3173-4a6f-af60-04e1f2e29868.htseq.counts
1.2437204
treatedM_05ac7b05-e459-4833-97fc-530185a7a55f.htseq.counts
0.5694407
treatedM_063411ca-874c-49ff-85c2-b6946f43df4e.htseq.counts
1.7741587
treatedM_1c6bb58a-3869-41f5-9fef-9070bbfedbad.htseq.counts
1.0302219
treatedM_51c2d807-7ee6-4b42-a6a7-3eb14f987bc0.htseq.counts
0.8420236
treatedM_57ef7745-338c-4fb2-ac9b-9f57daba2c51.htseq.counts
0.7137008
treatedM_77b9a94e-aaa7-409c-9849-86ba7d5c061a.htseq.counts
1.1492223
treatedM_79f4b12a-ed1c-46d5-ae3d-a0422454eaf4.htseq.counts
1.1795532
treatedM_7f86ace4-9a17-4b17-8a79-3b2ef53f62c3.htseq.counts
0.4929173
treatedM_9700948e-21f5-42ae-9cb5-f18c745887ce.htseq.counts
0.7685227
treatedM_a3a6543b-ea79-4724-a140-c83392b1842b.htseq.counts
0.7229443
treatedM_b5d92dd1-d98b-4b9e-953e-81c04dfb1e0d.htseq.counts
1.6718995
treatedM_bdd35388-0850-40ac-a21a-6bfee0c34eef.htseq.counts
1.1236795
treatedM_c49c187f-98e7-463d-a18b-accf3205eb81.htseq.counts
0.8690989
treatedM_c4c4578a-f1c2-462a-873f-c8db84fe3a6f.htseq.counts
0.7997849
treatedM_c7e1465f-cc5a-4dc9-9bb1-6f1112da7aad.htseq.counts
1.3488743
treatedM_dad3353e-6721-4e2e-ae06-04eea4c00f12.htseq.counts
1.4028708
treatedM_dcf5a350-7cff-475a-8f53-376422f990eb.htseq.counts
0.9744491
treatedM_e48c520a-d62c-440d-b17c-5b5ff03d41a2.htseq.counts
1.0464805
treatedM_e4d48564-e021-4bbc-bc00-145e9d64b501.htseq.counts
1.2870820
untreatedF_0d9e4b58-c22f-4de0-8b0d-0b5a8e19a1af.htseq.counts
0.9459732
untreatedF_104c7c7e-583a-4496-a685-5d277e28c11b.htseq.counts
1.1775898
untreatedF_10a32136-8a4d-4af3-8e70-543b2a336c91.htseq.counts
0.6152353
untreatedF_16fc8611-259d-4cd8-9e93-60d97bebb6bf.htseq.counts
1.2391675
untreatedF_21a0ef5f-24f5-45fe-ac84-deed4ef25329.htseq.counts
1.2718873
untreatedF_2f3f2e57-4c69-469f-be9c-01cc4f704427.htseq.counts
0.8374655
untreatedF_3e0831c0-6726-4f71-9cba-faaeea280cf9.htseq.counts
1.3554672
untreatedF_6c86f9ae-9e88-4eae-9ba3-4866943cca7f.htseq.counts
1.1739458
untreatedF_73756e81-79a2-4bff-96e7-99f038306b3e.htseq.counts
1.2774824
untreatedF_8d78e34d-490b-4f2e-ba1a-427afe96d26e.htseq.counts
1.1182581
untreatedF_92ee9ee1-8f03-4b53-b6c2-04d22fd692ae.htseq.counts
0.9974631
untreatedF_98380eb7-4de1-4e3a-989c-eedf6eec2977.htseq.counts
0.9686442
untreatedF_99e82612-a602-459b-8a60-80a812bf9d88.htseq.counts
0.9186733
untreatedF_b39bcb10-164d-48c5-89ba-9ef668765db6.htseq.counts
1.0414099
untreatedF_b49faa57-5a15-4193-9552-130d9a63eb40.htseq.counts
1.9690003
untreatedF_d2bf6185-08a5-4a73-9f87-7164cca1ccf6.htseq.counts
0.3466269
untreatedF_edf02d6d-619f-4325-a585-dca7bf5afbeb.htseq.counts
0.6032958
untreatedF_eed21a5a-30db-45a0-9b60-3f6a91b38cb6.htseq.counts
1.6426240
untreatedF_f7a956f8-4dc2-437a-92db-8baf8f9b3f14.htseq.counts
1.3828281
untreatedF_ffeed225-c2a3-4b4c-954c-4816903782a9.htseq.counts
1.4924352
untreatedM_09f395dd-d007-41e0-8a51-a6533ae51087.htseq.counts
0.9326201
untreatedM_10d6deec-fd89-4115-92f5-c9def40428b5.htseq.counts
1.0461914
untreatedM_1d782a72-61f3-4002-bee5-67daeb8244e3.htseq.counts
0.7269116
untreatedM_2cc2e3ce-68cd-4690-9fff-5ecf86c2f57a.htseq.counts
0.8120974
untreatedM_35a2858e-9787-457c-9435-33bd62332aa3.htseq.counts
1.1659125
untreatedM_78e27399-eb4a-4b86-992e-3e052bad044c.htseq.counts
1.1804585
untreatedM_89eb1fc8-6b38-446e-a4b0-fb9c520298dc.htseq.counts
0.9953164
untreatedM_9700948e-21f5-42ae-9cb5-f18c745887ce.htseq.counts
0.7685227
untreatedM_a3a6543b-ea79-4724-a140-c83392b1842b.htseq.counts
0.7229443
untreatedM_ad69efb1-61ab-4492-8468-9006dd00e915.htseq.counts
1.2424108
untreatedM_b04fb368-4b93-4ef9-ae9f-020484c1686b.htseq.counts
1.1802222
untreatedM_b1820867-e6b2-4228-ad00-19d1d7fddf67.htseq.counts
0.9759412
untreatedM_bdd35388-0850-40ac-a21a-6bfee0c34eef.htseq.counts
1.1236795
untreatedM_cfb40dba-37a7-4ae7-a2c9-34becc30f939.htseq.counts
0.8603649
untreatedM_d1cbb686-93d8-4349-8bf5-756887c60ebc.htseq.counts
0.9785968
untreatedM_d7a9f4a9-6998-45dc-9bc4-06f3f7110429.htseq.counts
0.9956183
untreatedM_e035a46e-6114-4a64-b5ae-9e6209223493.htseq.counts
1.1573965
untreatedM_e208fa4d-3f65-4615-bd6e-870a52bb6da3.htseq.counts
1.3581346
untreatedM_fbbb6d26-8bd4-40da-870b-d8db0f653cfa.htseq.counts
0.4067045
untreatedM_fe76a5ca-f70a-4ab7-b080-5a19ae36dc2b.htseq.counts
0.6536569 extracting the matrix [βir] for all genes i and model coefficients r
head(coef(dds)) Intercept condition_treated_vs_untreated
ENSG00000000003.13 12.217232 -0.199787925
ENSG00000000005.5 2.196920 -0.142444941
ENSG00000000419.11 10.229280 -0.088311028
ENSG00000000457.12 9.427934 0.003435401
ENSG00000000460.15 8.198034 0.407873374
ENSG00000000938.11 8.038019 0.102227993B prior variance
attr(dds, "betaPriorVar")[1] 1e+06 1e+06General Information (packages, info on log fold change shrinkage)
priorInfo(resLFC)$type
[1] "apeglm"
$package
[1] "apeglm"
$version
[1] ‘1.16.0’
$prior.control
$prior.control$no.shrink
[1] 1
$prior.control$prior.mean
[1] 0
$prior.control$prior.scale
[1] 0.001
$prior.control$prior.df
[1] 1
$prior.control$prior.no.shrink.mean
[1] 0
$prior.control$prior.no.shrink.scale
[1] 15
$prior.control$prior.var
[1] 1e-06priorInfo(resNorm)$type
[1] "normal"
$package
[1] "DESeq2"
$version
[1] ‘1.34.0’
$betaPriorVar
Intercept conditiontreated
1.000000e+06 1.974018e-01 priorInfo(resAsh)$type
[1] "ashr"
$package
[1] "ashr"
$version
[1] ‘2.2.51’
$fitted_g
$pi
[1] 0.9894591706 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
[9] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0057666315
[17] 0.0045680277 0.0000000000 0.0002061701 0.0000000000 0.0000000000 0.0000000000 0.0000000000
$mean
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
$sd
[1] 0.005907522 0.008354497 0.011815044 0.016708995 0.023630087 0.033417990 0.047260175 0.066835980
[9] 0.094520349 0.133671960 0.189040698 0.267343919 0.378081397 0.534687839 0.756162793 1.069375678
[17] 1.512325587 2.138751356 3.024651174 4.277502711 6.049302348 8.555005423 12.098604695
attr(,"class")
[1] "normalmix"
attr(,"row.names")
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Dispersion prior variance
dispersionFunction(dds)function (q)
coefs[1] + coefs[2]/q
<bytecode: 0x7fa76e1f23b8>
<environment: 0x7fa754a4a078>
attr(,"coefficients")
asymptDisp extraPois
0.8494044 4.5394083
attr(,"fitType")
[1] "parametric"
attr(,"varLogDispEsts")
[1] 1.20359
attr(,"dispPriorVar")
[1] 1.177617attr(dispersionFunction(dds), "dispPriorVar")[1] 1.177617metadata(dds)[["version"]][1] ‘1.34.0’Sample/Gene-dependent Nomralization factors
normFactors <- matrix(runif(nrow(dds)*ncol(dds),0.5,1.5),
ncol=ncol(dds),nrow=nrow(dds),
dimnames=list(1:nrow(dds),1:ncol(dds)))
normFactors <- normFactors / exp(rowMeans(log(normFactors)))
normalizationFactors(dds) <- normFactorscoldata <- DataFrame(grp=factor(rep(c("X","Y"),each=6)),
ind=factor(rep(1:6,each=2)),
cnd=factor(rep(c("A","B"),6)))
coldataDataFrame with 12 rows and 3 columns
grp ind cnd
<factor> <factor> <factor>
1 X 1 A
2 X 1 B
3 X 2 A
4 X 2 B
5 X 3 A
... ... ... ...
8 Y 4 B
9 Y 5 A
10 Y 5 B
11 Y 6 A
12 Y 6 Bas.data.frame(coldata)coldata$ind.n <- factor(rep(rep(1:3,each=2),2))
as.data.frame(coldata)model.matrix(~ grp + grp:ind.n + grp:cnd, coldata) (Intercept) grpY grpX:ind.n2 grpY:ind.n2 grpX:ind.n3 grpY:ind.n3 grpX:cndB grpY:cndB
1 1 0 0 0 0 0 0 0
2 1 0 0 0 0 0 1 0
3 1 0 1 0 0 0 0 0
4 1 0 1 0 0 0 1 0
5 1 0 0 0 1 0 0 0
6 1 0 0 0 1 0 1 0
7 1 1 0 0 0 0 0 0
8 1 1 0 0 0 0 0 1
9 1 1 0 1 0 0 0 0
10 1 1 0 1 0 0 0 1
11 1 1 0 0 0 1 0 0
12 1 1 0 0 0 1 0 1
attr(,"assign")
[1] 0 1 2 2 2 2 3 3
attr(,"contrasts")
attr(,"contrasts")$grp
[1] "contr.treatment"
attr(,"contrasts")$ind.n
[1] "contr.treatment"
attr(,"contrasts")$cnd
[1] "contr.treatment"Count outlier detection
W <- res$stat
maxCooks <- apply(assays(dds)[["cooks"]],1,max)
idx <- !is.na(W)
plot(rank(W[idx]), maxCooks[idx], xlab="rank of Wald statistic",
ylab="maximum Cook's distance per gene",
ylim=c(0,5), cex=.4, col=rgb(0,0,0,.3))
m <- ncol(dds)
p <- 3
abline(h=qf(.99, p, m - p))
Filtering criteria
plot(res$baseMean+1, -log10(res$pvalue),
log="x", xlab="mean of normalized counts",
ylab=expression(-log[10](pvalue)),
ylim=c(0,30),
cex=.4, col=rgb(0,0,0,.3))
Histogram p-values for all tests
use <- res$baseMean > metadata(res)$filterThreshold
h1 <- hist(res$pvalue[!use], breaks=0:50/50, plot=FALSE)
h2 <- hist(res$pvalue[use], breaks=0:50/50, plot=FALSE)
colori <- c(`do not pass`="khaki", `pass`="powderblue")barplot(height = rbind(h1$counts, h2$counts), beside = FALSE,
col = colori, space = 0, main = "", ylab="frequency")
text(x = c(0, length(h1$counts)), y = 0, label = paste(c(0,1)),
adj = c(0.5,1.7), xpd=NA)legend("topright", fill=rev(colori), legend=rev(names(colori)))
Section 2: Next Steps
My next steps are clean-up and understanding the analysis. I also have to zip/tar my data file.
Section 3: Data
I will have to tar/zip my data file and share them on the Google Drive closer to the due date.
Section 4: Known issues
- When I download my “cart” from https://portal.gdc.cancer.gov/, I have to unzip each individual file myself. I think using linux there is a faster way, but I am not sure how to do this yet. For now, I have unzipped each HTSeq file one by one.
- The file names are very long because each sample has a unique ID. Because of this, I needed to cut Sample ID’s out of my heatmaps because it was not loading the graph successfully. I will need to figure out a easy way to rename all the sample ID’s.
- I still need to figure out how to tar/zip my data file for the final project submission.
- I still need to clean up my analysis throughout the vignette.
---
title: "R Notebook"
output: html_notebook
---
# Milestone 2 

# Section 1: Update
The main updates I have for Milestone 2 are: I have ran through the entire vignette and have added analysis throughout, I have increased my sample size to a total of 80 samples, and I have also added Sex as a multiple comparison. 

## htseq-count input
I have imported a total of 80 htseq-count files into a directory, and is displayed in table sampleTable. There are n = 40 from each treatment group in my data file (20 Female, 20 Male for mulitple comparisons).

## Creating a variable called 'directory' which points to where my output files are located.
```{r}
directory <- "/Users/maricarmen/Desktop/files"
```

## Specifying files that contain "treated" in the filename, and using that to identify the condition status.
```{r}
sampleFiles <- grep("treated",list.files(directory),value=TRUE)
sampleCondition <- sub("(.*treated).*","\\1",sampleFiles)
sampleType <- sub(".*treated","",sampleFiles)
sampleTable <- data.frame(sampleName = sampleFiles,
                          fileName = sampleFiles,
                          condition = sampleCondition,
                          type = sampleType)
sampleTable$condition <- factor(sampleTable$condition)
sampleTable$type <- factor(sampleTable$type)
```

## Building the DESeqDataSet
```{r}
library("DESeq2")
ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
                                       directory = directory,
                                       design= ~ condition)
ddsHTSeq
```

# Pre-filtering
```{r}
keep <- rowSums(counts(ddsHTSeq)) >= 10
dds <- ddsHTSeq[keep,]
```

# Setting factor levels
```{r}
dds$condition <- factor(dds$condition, levels = c("untreated","treated"))
```

# Differential Expression Analysis
```{r}
dds <- DESeq(dds)
res <- results(dds)
res <- results(dds, name="condition_treated_vs_untreated")
res
```

# Log fold change shrinkage
```{r}
resultsNames(dds)
```


```{r}
resLFC <- lfcShrink(dds, coef="condition_treated_vs_untreated", type="apeglm")
resLFC
```

# Ordering results table (by p-values)
```{r}
resOrdered <- res[order(res$pvalue),]
```
```{r}
summary(res)
```
# How many p-values are less than 0.1?
```{r}
sum(res$padj < 0.1, na.rm=TRUE)
```

# Adjusting p-value to 0.5
```{r}
res05 <- results(dds, alpha=0.05)
summary(res05)
```

# How many p-values are less than 0.5?
```{r}
sum(res05$padj < 0.05, na.rm=TRUE)
```

# MAPlot for shrunkin log2 fold changes
```{r}
plotMA(resLFC, ylim=c(-2,2))
idx <- identify(res$baseMean, res$log2FoldChange)
rownames(res)[idx]
```

# Alternative shrinkage estimators
```{r}
resultsNames(dds)
```

# Setting 'coef = 2' (this may take awhile, grab a coffee)
```{r}
resNorm <- lfcShrink(dds, coef=2, type="normal")
resAsh <- lfcShrink(dds, coef=2, type="ashr")
```

```{r}
resNorm <- lfcShrink(dds, coef=2, type="normal")
resAsh <- lfcShrink(dds, coef=2, type="ashr")
```

```{r}
par(mfrow=c(1,3), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-3,3)
plotMA(resLFC, xlim=xlim, ylim=ylim, main="apeglm")
plotMA(resNorm, xlim=xlim, ylim=ylim, main="normal")
plotMA(resAsh, xlim=xlim, ylim=ylim, main="ashr")
```
# Plot counts
```{r}
plotCounts(dds, gene=which.min(res$padj), intgroup="condition")
```
# Plotting using ggplot
```{r}
d <- plotCounts(dds, gene=which.min(res$padj), intgroup="condition", 
                returnData=TRUE)
library("ggplot2")
ggplot(d, aes(x=condition, y=count)) + 
  geom_point(position=position_jitter(w=0.1,h=0)) + 
  scale_y_log10(breaks=c(25,100,400))
```
# Multi-factor design 
# Our sample population has a condition of interest (treated for liver cancer or untreated), but we also have the Sex of each patient under "type". By using the colData function, we see that we have to chop/clean up our Type names.
```{r}
colData(dds)
```

# First, I create a copy of the DDS so we can run a multi-factor analysis. 
```{r}
ddsMF <- dds
```

# Then, we change how "Type" is displayed for each sample participant. 
```{r}
levels(ddsMF$type)
```
# We will chop/clean up "Type" so that it only specifies F for female patient or M for male patient. 
```{r}
levels(ddsMF$type) <- sub("_.*", "", levels(ddsMF$type))
levels(ddsMF$type)
```
# As condition is the variable of interest, we put it at the end of our formula. We then re-run DeSeq analysis:
```{r}
design(ddsMF) <- formula(~ type + condition)
ddsMF <- DESeq(ddsMF)
```
# We access results using the results function:
```{r}
resMF <- results(ddsMF)
head(resMF)
```
# Now we use the contrast function to extract log2 fold changes while specifying Type
```{r}
resMFType <- results(ddsMF,
                     contrast=c("type", "F", "M"))
head(resMFType)
```


# Info on results columns
```{r}
mcols(res)$description
```

# Exporting to CSV file (when ran, this will expor CSV to your Desktop)
```{r}
write.csv(as.data.frame(resOrdered), 
          file="condition_treated_results.csv")
```

# Data transformations and visualizations!
# Extracting transformed values (note: this takes a VERY long time to run, take a break go get a coffee)
```{r}
vsd <- vst(dds, blind=FALSE)
rld <- rlog(dds, blind=FALSE)
head(assay(vsd), 3)
```

# Effects on transformation on variance (Standard Deviation) for the normal logarithm transformation (ntd)
```{r}
# this gives log2(n + 1)
ntd <- normTransform(dds)
library("vsn")
meanSdPlot(assay(ntd))
```
# Effects on transformation on variance (Standard Deviation) for the variance stabilizing transformation (vsd)
```{r}
meanSdPlot(assay(vsd))
```
# Effects on transformation on variance (Standard Deviation) for the regularized log transformation (rld)
```{r}
meanSdPlot(assay(rld))
```

# Heatmap of count matrix
# Known issue: I had to remove colnames because they were too complicated, and the heatmap wasn't being displayed. Will need to come back to this. Other known issue: Not sure why female and male are not clumped together, I think this has to do with how my files are ordered. I need to come back to this. 
# This is a heatmap with both condition and type being analyzed. Multi-factor design. 
```{r}
library("pheatmap")
select <- order(rowMeans(counts(ddsMF,normalized=TRUE)),
                decreasing=TRUE)[1:20]
df <- as.data.frame(colData(ddsMF)[,c("condition","type")])
pheatmap(assay(ntd)[select,], cluster_rows=FALSE, show_rownames=FALSE, show_colnames = FALSE,
         cluster_cols=FALSE, annotation_col=df)
```
# Heatmap using ntd data described above. 
```{r}
library("pheatmap")
select <- order(rowMeans(counts(dds,normalized=TRUE)),
                decreasing=TRUE)[1:20]
df <- as.data.frame(colData(ddsMF)[,c("condition","type")])
pheatmap(assay(ntd)[select,], cluster_rows=FALSE, show_rownames=FALSE, show_colnames = FALSE,
         cluster_cols=FALSE, annotation_col=df)
```
# Heatmap using vsd data described above.
```{r}
pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=FALSE, show_colnames = FALSE,
         cluster_cols=FALSE, annotation_col=df)
```
# Heatmap using rld data described above.
```{r}
pheatmap(assay(rld)[select,], cluster_rows=FALSE, show_rownames=FALSE, show_colnames = FALSE,
         cluster_cols=FALSE, annotation_col=df)
```

# Sample clustering 
```{r}
sampleDists <- dist(t(assay(vsd)))
```
# Creating a heatmap of the sample-to-sample distances.
```{r}
library("pheatmap")
library("RColorBrewer")
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$condition)
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows=sampleDists,
         clustering_distance_cols=sampleDists,
         col=colors)
```
# PCA plot using vsd 
```{r}
plotPCA(vsd, intgroup="condition", ntop = 500, returnData = FALSE)
```
# Variations to the standard workflow!
# Performing Wald test on dataset
```{r}
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomWaldTest(dds)
```
```{r}
resultsNames(dds)
```

# Performing Liklihood ratio test on dataset
```{r}
dds <- DESeq(dds, test="LRT", reduced=~1)
res <- results(dds)
```

# False sign or small rate in MA-plot
```{r}
resApeT <- lfcShrink(dds, coef=2, type="apeglm", lfcThreshold=1)
plotMA(resApeT, ylim=c(-3,3), cex=.8)
abline(h=c(-1,1), col="dodgerblue", lwd=2)
```

```{r}
summary(res)
```

# Dispersion plot and fitting alternatives!
# Dispersion plot
```{r}
plotDispEsts(dds)
```
# Custom dispersion fit 
```{r}
ddsCustom <- dds
useForMedian <- mcols(ddsCustom)$dispGeneEst > 1e-7
medianDisp <- median(mcols(ddsCustom)$dispGeneEst[useForMedian],
                     na.rm=TRUE)
dispersionFunction(ddsCustom) <- function(mu) medianDisp
ddsCustom <- estimateDispersionsMAP(ddsCustom)
```
# Independent filtering of results
```{r}
metadata(res)$alpha
```
```{r}
metadata(res)$filterThreshold
```

# Supplying a custom dispersion fit
```{r}
ddsCustom <- dds
useForMedian <- mcols(ddsCustom)$dispGeneEst > 1e-7
medianDisp <- median(mcols(ddsCustom)$dispGeneEst[useForMedian],
                     na.rm=TRUE)
dispersionFunction(ddsCustom) <- function(mu) medianDisp
ddsCustom <- estimateDispersionsMAP(ddsCustom)
```


# Independent filtering of results
```{r}
plot(metadata(res)$filterNumRej, 
     type="b", ylab="number of rejections",
     xlab="quantiles of filter")
lines(metadata(res)$lo.fit, col="red")
abline(v=metadata(res)$filterTheta)
```
# Turn off independent filtering
```{r}
resNoFilt <- results(dds, independentFiltering=FALSE)
addmargins(table(filtering=(res$padj < .1),
                 noFiltering=(resNoFilt$padj < .1)))
```

# 4 possible alternative hypotheses in MA-plot (note: takes long time to run, go grab a coffee. Also, make sure to turn off independent filtering, and reload/rerun Wald test)
```{r}
par(mfrow=c(2,2),mar=c(2,2,1,1))
ylim <- c(-2.5,2.5)
resGA <- results(dds, lfcThreshold=.5, altHypothesis="greaterAbs")
resLA <- results(dds, lfcThreshold=.5, altHypothesis="lessAbs")
resG <- results(dds, lfcThreshold=.5, altHypothesis="greater")
resL <- results(dds, lfcThreshold=.5, altHypothesis="less")
drawLines <- function() abline(h=c(-.5,.5),col="dodgerblue",lwd=2)
plotMA(resGA, ylim=ylim); drawLines()
plotMA(resLA, ylim=ylim); drawLines()
plotMA(resG, ylim=ylim); drawLines()
plotMA(resL, ylim=ylim); drawLines()
```

# Access to all calculated values
```{r}
mcols(dds,use.names=TRUE)[1:4,1:4]
```

```{r}
substr(names(mcols(dds)),1,10) 
```

```{r}
mcols(mcols(dds), use.names=TRUE)[1:4,]
```

# Mean values and Cook's distances
```{r}
head(assays(dds)[["mu"]])
```

```{r}
head(assays(dds)[["cooks"]])
```

# Dispersion
```{r}
head(dispersions(dds))
```

```{r}
head(mcols(dds)$dispersion)
```

# Size factors
```{r}
sizeFactors(dds)
```
# extracting the matrix [βir] for all genes i and model coefficients r
```{r}
head(coef(dds))
```
# B prior variance
```{r}
attr(dds, "betaPriorVar")
```

# General Information (packages, info on log fold change shrinkage)
```{r}
priorInfo(resLFC)
```
```{r}
priorInfo(resNorm)
```

```{r}
priorInfo(resAsh)
```
# Dispersion prior variance
```{r}
dispersionFunction(dds)
```
```{r}
attr(dispersionFunction(dds), "dispPriorVar")
```
```{r}
metadata(dds)[["version"]]
```


# Sample/Gene-dependent Nomralization factors
```{r}
normFactors <- matrix(runif(nrow(dds)*ncol(dds),0.5,1.5),
                      ncol=ncol(dds),nrow=nrow(dds),
                      dimnames=list(1:nrow(dds),1:ncol(dds)))

normFactors <- normFactors / exp(rowMeans(log(normFactors)))
normalizationFactors(dds) <- normFactors
```

```{r}
coldata <- DataFrame(grp=factor(rep(c("X","Y"),each=6)),
                       ind=factor(rep(1:6,each=2)),
                      cnd=factor(rep(c("A","B"),6)))
coldata
```
```{r}
as.data.frame(coldata)
```
```{r}
coldata$ind.n <- factor(rep(rep(1:3,each=2),2))
as.data.frame(coldata)
```

```{r}
model.matrix(~ grp + grp:ind.n + grp:cnd, coldata)

```

# Count outlier detection
```{r}
W <- res$stat
maxCooks <- apply(assays(dds)[["cooks"]],1,max)
idx <- !is.na(W)
plot(rank(W[idx]), maxCooks[idx], xlab="rank of Wald statistic", 
     ylab="maximum Cook's distance per gene",
     ylim=c(0,5), cex=.4, col=rgb(0,0,0,.3))
m <- ncol(dds)
p <- 3
abline(h=qf(.99, p, m - p))
```
# Filtering criteria
```{r}
plot(res$baseMean+1, -log10(res$pvalue),
     log="x", xlab="mean of normalized counts",
     ylab=expression(-log[10](pvalue)),
     ylim=c(0,30),
     cex=.4, col=rgb(0,0,0,.3))
```
# Histogram p-values for all tests
```{r}
use <- res$baseMean > metadata(res)$filterThreshold
h1 <- hist(res$pvalue[!use], breaks=0:50/50, plot=FALSE)
h2 <- hist(res$pvalue[use], breaks=0:50/50, plot=FALSE)
colori <- c(`do not pass`="khaki", `pass`="powderblue")
```
```{r}
barplot(height = rbind(h1$counts, h2$counts), beside = FALSE,
        col = colori, space = 0, main = "", ylab="frequency")
text(x = c(0, length(h1$counts)), y = 0, label = paste(c(0,1)),
     adj = c(0.5,1.7), xpd=NA)
legend("topright", fill=rev(colori), legend=rev(names(colori)))
```
# Section 2: Next Steps
My next steps are clean-up and understanding the analysis. I also have to zip/tar my data file.

# Section 3: Data
I will have to tar/zip my data file and share them on the Google Drive closer to the due date. 

# Section 4: Known issues
1) When I download my "cart" from https://portal.gdc.cancer.gov/, I have to unzip each individual file myself. I think using linux there is a faster way, but I am not sure how to do this yet. For now, I have unzipped each HTSeq file one by one.
2) The file names are very long because each sample has a unique ID. Because of this, I needed to cut Sample ID's out of my heatmaps because it was not loading the graph successfully. I will need to figure out a easy way to rename all the sample ID's. 
3) I still need to figure out how to tar/zip my data file for the final project submission. 
4) I still need to clean up my analysis throughout the vignette. 